Geometric models use the concept of instance space The most obvious example of geometric models is when all the features are numerical and can become coordinates in a Cartesian coordinate system. When we only have two or three features they are easy as visualise. However since many machine learning problems have hundreds or thousands of features and therefore dimensions, visualising these spaces is impossible. However many of the geometric concepts, such as linear transformations, still apply in this hyper space.This can help us better understand our models. For instance we expect many learning algorithms will be translation invariant, that is it does not matter where we place the origin in the coordinate system. Also we can use the geometric concept of euclidean distance to measure similarity between instances, this gives us a method to cluster like instances and form a decision boundary between them.
Supposing we are using our linear classifier to classify paragraphs as either happy or sad and we have devised a set of tests. Each test is associated with a weight ,w, to determine how much each test contributes to the overall result.
We can simply sum each test, multiplied by its weight, to get an overall score and create a decision rule that will create a boundary, say if the happy score is greater than a threshold t.
Each feature contributes independently to the overall result, hence the rules linearity. This contribution depends on each features relative weights. These weights can be positive or negative and each individual feature is not subject to the threshold in calculating the overall score.
In practice real data is noisy and not necessarily that easily separable. Even when data is easily separable a particular decision boundary may not have much meaning. Consider data that is sparse, such as in text classification where the number of words is large compared to the number of instances of each word. In this large area of empty instance space it may be easy to find a decision boundary but which is the best one? One way is to use a margin to measure the distance between the decision boundary and its closest instance. We will explore these techniques later in the book.
A typical example of a probabilistic model is the Bayesian classifier. Given some training data, D, and a probability based on an initial training set, a particular hypothesis, h, called the posteriori probability, P (h/D).
As an example consider we have a bag of marbles. We know that 40% of them are red and 60% are blue. We also know that of the red marbles half of them have flecks of white and all the blue marbles have flecks. When we reach into the bag to select a marble we can feel by its texture that it has flecks. What are the chances it is red?
Let P(RF) = the probability that a randomly drawn marble with flecks is red.
Probabilistic models allow us to allow us to explicitly calculate probabilities, rather than just a binary true or false. As we know the key thing we need to do is create a model that maps or features variable to a target variable. When we take a probabilistic approach we assume there is an underlying random process that creates a well defined, but unknown, probability distribution.
Consider a spam detector. Our feature variables, X, could consist of a set of words that indicate the email might be spam, The target variable Y is the instance class: spam or ham. We are interested in the conditional probability of Y given X. For each email instance there will be a feature vector, X, consisting of boolean values representing the presence of our spam words. We are trying to find Y our target boolean representing spam or not spam.
We can see that once we begin adding more words to our feature vector it will quickly grow unmanageable. With a feature vector of n size we will have 2n cases to distinguish. Fortunately there are other methods to deal with this problem as we shall see later.
The probabilities in the above table are known as posterior probabilities. These are used when we have knowledge from a prior distribution. For instance that one in ten emails is spam. However consider a case where we may know that X contains x2= 1 but we are unsure of the value of x1 This instance could belong in row two where the probability of it being spam is 0.7, or in row 4 where the probability is 0.8. The solution is to average these two rows using the probability of x1 =1 in any instance. That is the probability that a word, x1, will appear in any email, spam or not.
This is an example of a likelihood function: P(X|Y). So why do want to know the probability of X , something we all ready know, conditioned on Y something we know nothing about. A way to look at this is to consider the probability of any email containing a particular random paragraph, say the the 127th paragraph of War and Peace. Clearly this probability is small regardless if the email is spam or not. What we are really interested in is not the magnitude of these likelihoods but their ratio. How much more likely is an email containing a particular combination of words likely to be spam or not spam. These are sometimes called generative models because we can sample across all the variables involved.
If the odds are less than one we assume that the class in the denominator the most likely. If it is greater than one then the class in the enumerator is the most likely. If we use the data from table 1.1 we calculate the following posterior odds.
The likelihood function is important in machine learning because it creates a generative model. If we know the probability distribution of each word in a vocabulary, together with the likelihood of each one appearing in either a spam or not spam email we can generate a random spam email according to the conditional probability P(X|Y = spam).
These logical rules can be organised into a tree structure. In fig we see that the instance space is iteratively partitioned at each branch. The leaves consist of rectangular areas (or hyper rectangles in the case of higher dimensions) representing segments of the instance space. Depending on the task we are solving the leaves are labelled with a class, probability, real number and so on.
Note that decision rules may overlap and make contradictory predictions.
They are then said to be logically inconsistent. Rules can also be incomplete in when they do not take in to account all the coordinates in the feature space. There are a number of ways we can address these issues and we will look at these in detail later in the book.
Since tree learning algorithms usually work in a top down manner the first task is to find a good feature to split on at the top of the tree. We need to find a split that will result in a higher degree of purity in subsequent nodes. By purity I mean the degree to which training examples all belong to the same class. As we descend down the tree, at each level we find the training examples at each node increase in purity, that is they increasingly become separated into their own classes until we reach the leaf where all examples belong to the same class.
To look at this another way we are interested in lowering the entropy of subsequent nodes in our decision tree. Entropy, a measure of disorder, is high at the top of the tree (the root) and is progressively lowered at each node as the data is divided up into its respective classes.
In more complex problems, those with larger feature sets and decision rules, finding the optimum splits is sometimes not possible, at least not in acceptable amount of time. We are really interested in creating the shallowest tree to reach our leafs in the shortest path. The time it takes to analyse each node grows exponentially with each additional feature, so the optimum decision tree may take longer to find than actually using a sub optimum tree to perform the task.
An important property of logical models is that they can, to some extent, provide an explanation for their predictions. For example consider the predictions made by a decision tree. By tracing the path from leaf to root we can determine the conditions that resulted in the final result. This is one of the advantages of logical models in that they can be inspected by a human to reveal more about the problem.
In the same way that decisions in real life are only as good as the information available to us, in a machine learning task the model is only as good as its features. Mathematically features are a functions that maps from the instance space to a set of values in a particular domain. In machine learning most measurements we make are numerical therefore the most common feature domain is the set of real numbers. Other common domains include boolean, true or false, or integers, say when we are counting the occurrence of a particular feature, or finite sets such as a set of colours or shapes.
Models are defined in terms of their features. Also single features can be turned into a model, known a s a univariate model. We can distinguishes between two use of features, related to the distinction between grouping and grading.
f(X) = 0, emails that do not contain x1, , f(X) > 0, emails that contain x1 one or more times and so on. These conditions are called binary splits because they divide the instance space in to two groups. Those that satisfy the condition and those that don't. We can also split the instance space into more than two segments to create non binary splits. For instance where f(X) = 0 ; 0 < F(X) < 5 ; F(X) > 5 and so on.
Since this rule is linear each feature makes an independent contribution to the score of an instance. This contribution depends on wi . If it is positive then an positive xi will increase the score. If wi is negative a positive xi decreases the score. If wi is small or zero then the contribution it makes to the overall result is negligible. It can be seen that the features make a measurable contribution to the final prediction.
These two uses of features, as splits (grouping) and predictors (grading) can be combined into one model. A typical example occurs when want to approximate a non linear function say: y sin π x on the interval -1 < x < 1. Clearly simple linear model will not work. Of course the simple answer is to split the x axis into -1 < x 0 and 0 < On each of these segments we can find a reasonable linear approximation.
A lot of work can be done improving our models performance by feature construction and transformation. In most machine learning problems the features are not necessarily explicitly available. They need to be construction from raw data sets and then transformed into something our model can make use of. This is especially important in problems like text classification. In our simple spam example we used what is known as a bag of words representation because it disregards the order of the words. However by doing this we loose important information about the meaning of the text.
An important part of feature construction is discretisation. We can sometimes extract more information, or information that is more relevant to our task by dividing features into relevant chunks. For instance supposing our data consists of a list of peoples precise income and we are trying to determine if there is a relationship between financial income and the suburb lived in. Clearly it would be appropriate if our feature set did not consist of precise incomes but ranges of income. Although strictly speaking we loose information, if we choose our intervals appropriately we will not loose information related to our problem, and our model will perform better and give us results that are more easily interpreted.
This highlights the major tasks of feature selection, separating the signal from the noise.
Real world data will invariable contain a lot of information we do not need as well as just plain random noise, and separating the, perhaps small, part of the data that is relevant to our needs is important to the success of our model. It is of course important that we do not throw out information that may be important to us.
Often our features will be non linear, and linear regression may not give us good results. A trick is to transform the instance space itself. Supposing we have data such as that shown in figure below Clearly linear regression only gives us a reasonable fit, shown in the figure on the left. However we can improve this result if we square the instance space, that is we make x = x2 and y = y2 , shown in the figure on the right.
We can go further and use a technique called the kernel trick. The idea is that we can create a higher dimensional implicit feature space. Pairs of data points are mapped from the raw data set to this higher dimensional space via a specified function, sometimes called a similarity function.
We can see that by squaring the dot product in the original 2D space we obtain the dot product in the 3D space without actually creating the feature vectors themselves. Here we have defined the kernel k(x1,x2) = (x1 •x2)2. Calculating the dot product in a higher dimensional space is often computationally cheaper and, as we will see, this technique is used quite widely in machine learning from support vector machines (SVM), principle component analysis (PCA) and correlation analysis.
Using a kernel allows us to classify each new instance by evaluating it paired with each training example, rather than on the mean of positive and negative example as is the case for the basic linear classifier. This allows us to create more flexible, non linear, decision boundaries.
A very interesting and important aspect is the interaction between features. One form of interaction is correlation. For example words in a blog post, where we perhaps might expect there to be positive correlation between the words 'winter' and 'cold', and a negative correlation between winter and hot. What this means for your model depends on your task. If you are doing a sentiment analysis, you might want to consider reducing the weights of each word if they appear together, since the addition of another correlated word would be expected to contribute marginally less weight to the over all result than if that word appeared by itself.
Also with regards to sentiment analysis we often need to transform certain features to capture their meaning. For example the phrase 'not happy' contains a word that would, if we just used 1-grams, contribute to a positive sentiment score when its sentiment is clearly negative. A solution, apart from using 2-grams which may unnecessarily complicate the model, would be to recognise when these two words appear in a sequence and create a new feature 'not_happy' with an associated sentiment score.
Selecting and optimising features is work well spent. It can be a significant part of the design of learning systems. This iterative nature of design flips between two phases. Firstly understanding the properties of the phenomena you are studying and secondly testing your ideas with experimentation. This experimentation gives us deeper insight into the phenomena, allow us to optimise our features, gain deeper understanding and so on until we are satisfied our model gives us an accurate reflection of reality.
So far we have introduced a broad cross-section of machine learning problems, techniques and concepts. Hopefully by now you have an idea about how to begin tackling a new and unique problem by breaking it up into its components. We have reviewed some of the essential mathematics and explored ways to visualise our designs. We can see that the same problem can have many different representations and each one may highlight different aspects. Before we can begin modelling we need a well defined objective phrased in terms of a specific, feasible and meaningful question. We need to be clear how we can phrase the question in a way a machine can understand.
We have seen that the design process, although consisting of different, distinct activities, is not necessarily a linear process, but more an iterative one. We cycle through each particular phase, proposing and testing ideas until we feel can jump to the next phase. Sometimes we may jump back to a previous stage. We may sit at an equilibrium point, waiting for a particular event to occur, we may cycle through stages, or go through several stages in parallel.
One of the most important components of a machine learning system is its data. In the same way humans learn by interpreting sensory information, a machine learn from the information retrieved from data. Data varies hugely in quality, availability and type. In many machine learning projects most of the time can be spent collecting, verifying and 'massaging' data in to a shape that is acceptable to our learning algorithms. In the next chapter we will look at the details of this vitally important aspect of machine learning.
We now have the following system of linear equations.
x1 = x2 = 0 ; x3 = 4 ; x4 = 3 ; z = 0.
Is this an optimum solution remembering that our goal is to maximise z? We can see that since z subtracts x1 and x2 in the first equation in our linear system we are able to increase these variables. If the coefficients in this equation were all non negative then there would be no way to increase z. Remember x1 ≥ 0, x2 ≥ 0, x3 ≥ 0 , x4 ≥ 0. So we will know that we have found an optimum solution when all coefficients in the objective equation are positive.
This is not the case here. So we take one of the non basic variables with a negative coefficient in the objective equation, say, x1, called the entering variable, and use a technique called pivoting to turn it from a non basic to a basic variable. At the same time we will change a basic variable, called the leaving variable, into a non basic. We can see that x1 appears in both the constraint equations so which one do we choose to pivot? Remembering we need to keep the coefficients positive. We find that by using the pivot element that yield the lowest ratio of right hand side of the equations to their respective entering coefficients then we can find another basic solution. For x1 in this example this gives us 4/2 for the first constraint and 3/1 for the second. So we will pivot using x1 in constraint 1.
We can now write this in terms of x1 and substitute it into the other equations to eliminate x1 from these equations. Once we have performed a bit of algebra we end up with the following linear system.
This gives us the solution of x3 = x4 = 0 ; x1 = 5/3 ; x2 = 2/3 ; and z = 7/3. This is the optimal solution because there are no more negatives in the first equation.
We can visualise this with a graph. The shaded area is the region where we will find a feasible solution.
An analogy is often made between systems design and designing other things such as a house. To a certain extent this analogy holds true. We are attempting to place design components into a structure that meets a specification. The analogy breaks down when we consider their respective operating environments. It is generally assumed in the design of a house that the landscape, once suitably formed, will not change.
Software environments are slightly different. Systems are interactive and dynamic. Any system we design will be nested inside other systems, either electronic, physical, or human. In the same way, the different layers in computer networks (application layer, transport layer, physical layer etc) nest different sets of meanings and function, so as to do activities we do at different levels of a project.
As the designer of these systems we must also have a strong awareness of the setting, the domain in which we work. This knowledge gives us clues to patterns in our data and helps us give context to our work.
Machine learning projects can be divided into 5 distinct activities.
The designer is mainly concerned with the first three. However they often play, and in many projects, must play a major role in other activities. It should also be said that a projects timeline is not necessarily a linear sequence of these activities. The important point is that they are distinct activities. They may occur in parallel with each other, and in other ways interact, but they generally involve different types of tasks that can be separated out in terms of human and other resources, stage of the project and externalities. Also we need to consider that different activities involve distinct operational modes. Consider the different ways your brain works when you are sketching out an idea compared to when you are working on a specific analytical task, say a piece of code.
Often the hardest question is where to begin? We can start drilling into the different elements of a problem, with an idea of a feature set, and perhaps an idea of the model or models we might use. This may lead to a defined object and specification, or we may have to do some preliminary research, such as checking possible data sets and sources, or available technologies or to talk to other engineers and technicians and users of the system. We need to explore the operating environment and various constraints, is it part of a web application, or a laboratory research tool for a scientists?
Selecting and optimising features is often a major activity (really a task in itself) for the machine learning designer. We cannot really decide what features we need until we have adequately described the task, and of course both the task and features are constrained by the types of feasible models we can build.
As designers we are asked to solve a problem. We are given some data and an expected output, a solution. The first step is to frame the problem in a way that a machine can understand and in a way that carries meaning for a human. There are the following six broad approaches we can take to precisely defining our machine learning problem.
1.Exploratory: Here we are analysing data looking for patterns, such as a trend or relationship between variables. Exploration often will lead to a hypothesis such as linking diet with disease, or crime rate with suburb.
2.Descriptive: Here we are trying to summarise specific features of our data. For instance, the average life expectancy, average temperature or number of left handed people in a population.
3.Inferential: An inferential question is one that attempts to support a hypothesis, for instance proving (or disproving) a general link between life expectancy and income by using different data sets.
4, Predictive: Here we are trying to anticipate future behaviour. For instance predicting life expectancy by analysing income.
5.Casual: his is an attempt to find what causes something. Does low income cause a lower life expectancy?
Most machine learning problems involve several of these types of questionings during development. For instance we may first explore the data looking for patterns or trends, then we may describe certain key features of our data. This may enable us to make a prediction, find a cause or a mechanism behind a particular problem.
The question must be plausible and meaningful in its subject area. This domain knowledge enables you to understand the things that are important in your data, and see where a certain pattern or correlation has meaning.
The question should be as specific as is possible while still giving a meaningful answer. It is common for it to begin as a generalised statement, such as 'I wonder if wealthy means healthy'. So you do some further research and find you can get statistics for wealth by geographic region, say from the tax office. We can measure health by its inverse, illness, say by hospital admissions and test our initial proposition 'Wealthy means healthy' by tying illness to geographic region. We can see that a more specific question relies on several, perhaps questionable, assumptions.
We should also consider that our results may be confounded by the fact that poorer people may not have health care insurance so are less likely to attend a hospital despite illness. There is an interaction between what we want to find out and what we are trying to measure. This interaction perhaps hides a true rate of illness. All is not lost however. Because we know about these things then perhaps we can account for them in our model.
We can make things a lot easier by learning as much as we can about the domain we are working in.
You could possibly save yourself a lot of time by checking that the question you are asking, or part of it, has not already been answered, or that there are data sets available that may shed some light. Often you have to approach a problem from several different angles at once. Do as much preparatory research as you can. It is quite likely that other designers have done work that could shed light on your own.
A task is a specific activity conducted over a period of time. We have to distinguish between the human tasks, planning designing, implementing, to the machine tasks, classification, clustering, regression, and so on. Also consider when there is overlap between human and machine as, say, in selecting features for a model. Our goal really, in machine learning, is to transform as many of these tasks as we can, from human tasks to machine tasks.
It is not always easy to match a real world problem to a specific task. Many real world problems may seem to be conceptually linked, but require a very different methods to solve. Alternatively problems that appear completely different may require similar methods. Unfortunately there is no simple look up table to match a particular task to a problem. A lot depends on the setting and domain. A similar problem in one domain may be unsolvable in another, perhaps because of lack of data. There are however a small number of tasks that are applied to a large number of methods to solve many of the most common problem types. In other words in the space of all possible programming tasks there is a subset of tasks that are useful to our particular problem. Within this subset there is a smaller subset of tasks that are easy and can actually be applied usefully to our problem.
Let's first briefly introduce the basic machine tasks. Classification is probably the most common type of task, due in part because it is relatively easy, well understood and solves a lot of common problems. Multi class classification (for instance handwriting recognition) can sometimes be achieved by chaining binary classification tasks, however we lose information this way, and we become unable to define a single decision boundary. For this reason multi class classification is often treated separately to binary classification.
There are cases where what we are interested in is not discrete classes but a real number, for instance a probability. These type of problems are regression problems. Both classification and regression require a training set of correctly labelled data. They are supervised learning problems.
Clustering, on the other hand, the task of grouping items without any information on that group is an unsupervised learning task. Clustering is basically making a measurement of similarity.
Related to clustering is association, this is unsupervised task to find a certain type of pattern in a data. This task is behind movie recommender systems, and 'customers who bought this also bought ..' on checkout pages of online stores.
From these basic machine tasks there are a number of derived tasks. In many applications this may simply be applying the learning model to a prediction to establish a casual relationship. We must remember that explaining and predicting are not the same. A model can make a prediction but unless we know explicitly how it made the prediction we cannot begin to form a comprehensible explanation. An explanation requires human knowledge of the domain.
We can also use a prediction model to find exceptions from a general pattern Here we are interested in the individual cases that deviate from the predictions. This is often called anomaly detection and has wide applications in things like detecting bank fraud, noise filtering, even in the search for extraterrestrial life.
An important and potentially useful task is subgroup discovery. Our goal here is not, as in clustering, to partition the entire domain but rather find a subgroup that has a substantially different distribution. In essence subgroup discovery is trying to find relationships between a dependant, target variable an many independent explaining variables. We are not trying to find a complete relationship rather a group of instances that are different in ways that are important in the domain. For instance establishing a subgroup 'smoker = true' and 'family history =true' for a target variable of 'heart disease =true'.
Finally we consider control type tasks. These act to optimise control setting to maximise a pay off given different conditions. This can be achieved in several ways. We can clone expert behaviour, the machine learns directly from a human and makes predictions of actions given different conditions. The task is to learn a prediction model for the experts actions. This is similar to reinforcement learning where the task is to learn a relationship between conditions and optimal action.
In machine learning systems, software flaws can have very serious real world consequences, what happens if your algorithm embedded in an assembly line robot classifies a human as a production component. Clearly in critical systems you need to plan for failure. There should be a robust fault and error detection procedure embedded in your design process and systems.
Sometimes it is necessary to design quite complex systems simply for the purpose of debugging and checking for logic flaws. It may be necessary to generate data sets with specific statistical structure, or create 'artificial humans' to mimic an interface. For example, developing a methodology to verify that the logic of your design is sound at the data, model, and task levels. Errors can be hard to track, and as a scientist, you must assume there are errors and try to prove your hypothesis.
The idea of recognising and gracefully catching errors is important for the software designer, but as machine learning systems designers we must take it a step further. We need to be able to capture, in our models, the ability to learn from an error.
Consideration must be given to how we select our test set and in particular how representative it is of the rest of the data set. For instance if it is noisy compared to the training set, it will give poor results on the test set suggesting our model is over fitting, when in fact this is not the case. To avoid this a process of cross validation is used. This works by randomly dividing the data into, for example ten chunks of equal size. We use nine chunks for training the model and one for testing. We do this 10 times, using each chunk once for testing. Finally we take an average of test set performance. Cross validation is used with other supervised learning problems besides classification, but, as you would expect, unsupervised learning problems need to be evaluated differently.
Since with an unsupervised task, we do not have a labelled training set. Evaluation can therefore be a little more tricky since we do not know what a correct answer looks like. In a clustering problem for instance we can compare the quality of different models by measures such as the ratio of cluster diameter compared with the distance between clusters. However in problems of any complexity we can never tell if there is another model not yet built, that is better.
Decision variables, a set of controllable inputs. These inputs are varied within the specified constraints in order to satisfy the objective function.
Constraints. These are relations between decision variables and parameters. They define what values the decision variables can have.
Most optimisation problems have a single objective function. In the cases where we may have multiple objective functions we often find that they conflict with each other, for example reducing costs and increasing output. In practice we try to reformulate multiple objectives into a single function, perhaps by creating a weighted combination of objective functions. In our costs and output example a variable along the lines of cost per unit might work.
The decision variables are the variables we control to achieve the objective. They may include things like resources or labour. The parameters of the module are fixed for each run of the model. We may use several 'cases' where we choose different parameters to test variations in conditions.
There are literally thousands of solution algorithms to the many different types of optimisation problems. Most of them involve first finding a feasible solution, then iteratively improving on this ,by adjusting the decision variables, to, hopefully, find an optimum solution. Many optimisation problems can be solved reasonably well with linear programming techniques. They assume that the objective function and all the constraints are linear with respect to the decision variables. Where these relationships are not linear we often use a suitable quadratic function. If the system is non linear then the objective function may not be convex. That is it may have more than one local minima and there is no assurance that a local minima is a global minima.
Mr Clippy, turned on by default in early versions of Office, was almost universally derided by users of the software and could go down in history as one of machine learning's first big fails. The Smithsonian Magazine called Mr Clippy "one of the worst software design blunders in the annals of computing".
So why was the cheery Mr Clippy so hated? Clearly the folks at Microsoft, at the forefront of consumer software development, were not stupid, and the idea that an automated assistant could help with day to day office tasks is not necessarily a bad idea. Indeed later incarnations of automated assistants, the best ones at least, operate seamlessly in the background and provide a demonstrable increase in work efficiency. Consider predictive text. There are many examples, some very funny, of where predictive text has gone spectacularly wrong, but the majority of cases where it doesn't fail it goes unnoticed. It just becomes part of our normal work flow.
At this point we need a distinction between error and failure. Mr Clippy failed because it was obtrusive and poorly designed, not necessarily because it was in error i.e. it could make the right suggestion, but chances are you already know that you are writing a letter. Predictive text has a high error rate, i.e. it often gets the prediction wrong, but it does not fail largely because of the way it is designed to fail unobtrusively.
The design of any system that has a 'tightly coupled human interface', to use systems engineering speak, is difficult. Human behaviour, like the natural world in general, is not something we can always predict. Expression recognition systems, natural language processing and gesture recognition technology, amongst other things, all open up new ways of human machine interaction and this has important applications for the machine learning specialist.
In the first years of the web, search engines used a simple system based on the number of times search terms appeared in articles. Web developers soon began gaming the system, by increasing the number of key search terms. Clearly this would lead to keyword arms race and result in a very boring web. The page rank system, measuring the number of quality inbound links, was designed to provide a more accurate search result. Now, of course, modern search engines use more sophisticated, and secret, algorithms.
What is also important for ML designers is that the co-evolution of humans and algorithms has had an important by-product. A huge data resource that is, in many ways, beginning to distil some of the vastness of human experience. The power of algorithms to harvest this resource, to extract knowledge, insights that would not have been possible with smaller data sets, is massive. So many human interactions are now digitised and we are only just beginning to understand and explore the many ways this data can be used.
For this study the authors labelled each word (a 1gram) and associated it with a mood score and the year it was published. We can see that emotion words, such as joy, sadness, fear and so forth can be scored according to the positive or negative mood they evoke. The mood score was obtained from wordnet (wordnet.princeton.edu). Wordnet assigns an affect score to each mood word. Finally the authors simply counted the occurrences of each mood word.
Here ci is the count of a particular mood word, n is the total count of mood words (not all words just words with a mood score). Cthe is the count of the word 'the' in the text. This normalises the sum, to take into account that some years more books were written (or digitised). Also since many later books tend to contain more technical language the word 'the' was used normalize rather than total word count. This gives a more accurate representation of emotion over long time periods in prose text. Finally the score is normalised according to a normal distribution, Mz, by subtracting the mean and dividing by the standard deviation.
Here we can see one of the graphs generated by this study. It shows the joy-sadness score for books written in this period and clearly shows a negative trend associated with the period of world war 2.
This study is interesting for several reasons. Firstly it is an example of data driven science, where previously 'soft' sciences such as sociology and anthropology, are given a solid empirical footing. Despite some pretty impressive results, this study was relatively easy to implement. This is mainly because most of the hard work had already been done by wordnet and Google. This highlights how using data resources freely available on the internet and software tools such as the Pythons' data and machine learning packages, anyone one with the data skills and motivation is able build on this work.
In the past decades, many powerful machine learning algorithms have been developed for analyzing data and solving real-world problems. Machine learning systems allow us to utilize the large amounts of data that is available to us in the modern age of technology and help us with decision-making and predictions of future events.
In this chapter, we will learn more about the main concepts and different types of machine learning. Together with a basic introduction to the relevant terminology, we will lay the groundwork to successfully use machine learning techniques for practical problem solving.
In this age of modern technology there is one resource that we have in abundance: Large amounts of structured and unstructured data. In the second half of the twentieth century, machine learning evolved as a subfield of artificial intelligence that involves the development of self-learning algorithms to gain knowledge from this data in order to make predictions. Instead of requiring humans to manually derive rules and building models from analyzing large amounts of data, machine learning offers a more efficient alternative to capture knowledge in data for gradually improving the performance of predictive models and making data-driven decisions.
Not only is machine learning becoming increasingly important in computer science research but it also plays an ever-greater role in our everyday life. Thanks to machine learning, we enjoy robust email spam filters, convenient text and voice recognition, reliable web search engines, challenging chess players, and, hopefully soon, safe and efficient self-driving cars.
In this section, we will take a look at the three types of machine learning: Supervised learning, unsupervised learning, and reinforcement learning. We will learn about the fundamental differences between the three different learning types, and using conceptual examples, we will develop an intuition for the practical problem domains where these can be applied.
The main goal in supervised learning is to learn a model from labeled training data that allows us to make predictions about unseen or future data. Here, the term supervised refers to a set training of samples is where the desired output signals (labels) are already known.
Considering the example of email spam filtering, we can train a model using a supervised machine learning algorithm on a corpus of labeled emails to predict whether a new email belongs to the spam or non-spam category. A supervised learning task with discrete class labels, such as in the previous email spam-filtering example, is also called a classification task. Another subcategory of supervised learning is regression where the outcome signal is a continuous value.
Classification is a subcategory of supervised learning where our goal is to predict categorical class labels of new instances based on past observations. Those class labels are discrete, unordered values that can be understood as the "group memberships" of the instances. The previously mentioned example of email-spam detection represents a typical example of a binary classification task where the machine learning algorithm learns a set of rules in order to distinguish between two possible classes: spam and non-spam email.
However, the set of class labels does not have to be of binary nature. The predictive model learned by a supervised learning algorithm can assign any class label that was presented in the training dataset to a new, unlabeled instance. A typical example of a multi-class classification task is handwritten character recognition. Here, we could collect a training dataset that consists of multiple handwritten examples of each letter in the alphabet. Now, if a user provides a new handwritten character via an input device, our predictive model would be able to predict the correct letter in the alphabet with certain accuracy. However, our machine learning system would be unable to correctly recognize any of the digits 0 to 9, for example, if they were not part of our training dataset.
The following figure illustrates the concept of a binary classification task given 30 training samples: 15 training samples are labeled as "negative class" (red minus signs) and 15 training samples are labeled as "positive class" (green plus signs). In this scenario, our dataset is 2-dimensional, which means that each sample has two values associated with it, and . Now, we can use a supervised machine learning algorithm to learn a rule – the decision boundary represented as black dashed line – that can separate those two classes and classify new data into each of those two categories given its and values.
We learned in the previous section that the task of classification is to assign categorical, unordered labels to instances. A second type of supervised learning is the prediction of continuous outcomes, which is also called regression analysis. In regression analysis, we are given a number of predictor (explanatory) variables and a continuous response variable (outcome), and we are trying to find a relationship between those variables that allows us to predict an outcome.
For example, let's assume that we are interested in predicting the Math SAT scores of our students. If there is a relationship between the time spent studying for the test and the final scores, we could use it as training data to learn a model that uses the study time to predict the test scores of future students who are planning to take this test.
The term regression was devised by Francis Galton in his article Regression towards mediocrity in hereditary stature in 1886. Galton described the biological phenomenon that the variance of height in a population does not increase over time. He observed that the height of parents is not passed on to their children but the children's height is regressing towards the population mean.
The following figure illustrates the concept of linear regression. Given a predictor variable x and a response variable y, we are fitting a straight line to this data that minimizes the average distance between the sample points and the fitted line. We can now use the intercept and slope learned from this data to predict the outcome variable of new data.
Another type of machine learning is reinforcement learning. In reinforcement learning, the goal is to develop a system (agent) that improves its performance based on information that it receives from the environment; this feedback is also called reward signal. Reinforcement learning is related to supervised learning as it receives feedback. However, in reinforcement learning this feedback is not the correct ground truth label or value but a measure of how good the action was measured by a reward function. Through the interaction with the environment, an agent can then use reinforcement learning to learn a series of actions that maximize this reward via an exploratory trial-and-error approach or deliberative planning.
A popular example of reinforcement learning is a chess engine. Here, the agent decides upon a series of moves depending on the state of the board (the environment), and the reward can be defined as "win" or "lose" at the end of the game.
In supervised learning, we know the "right answer" beforehand when we train our model, and in reinforcement learning, we define a measure of reward for particular actions by the agent. In unsupervised learning, however, we are dealing with unlabeled data or data of unknown structure. Using unsupervised learning techniques, we are able explore the structure of our data to extract meaningful information without the guidance of a known outcome variable or reward function.
Clustering is an exploratory data analysis technique that allows us to organize a pile of information into meaningful subgroups (clusters) without having any prior knowledge of their group memberships. Each cluster that may arise during the analysis defines a group of objects that share a certain degree of similarity but are more dissimilar to objects in other clusters, which is why clustering is also sometimes called "unsupervised classification." Clustering is a great technique to structure information and derive meaningful relationships among data, For example, it allows marketers to discover customer groups based on their interests in order to develop distinct marketing programs.
The figure below illustrates how clustering can be applied to organize unlabeled data into three distinct groups based on similarities of their features and .


text:
Games CD Projekt Red has more big games on the line despite Cyberpunk...
CD Projekt Red has more big games on the line despite Cyberpunk 2077
CD Projekt Red have added Cyberpunk 2077 to their games list after being dependent on Witcher Series for many years. Today, they have confirmed that more games are coming.
CD Projekt Red President Adam Kicinski told Polish business site Stooq, that they are already working on a newer single-player game.
"We have already been working on another single player game, we have created a relatively clear concept that is waiting for further development. And this is already beginning to happen, although we do not want to spend time on it. Immediately after the work on 'Cyberpunk 2077' over the next title will kick off, "said the president.
Furthermore, he said that CD Projekt Red focuses on more Witcher Games. Adam Kicinski added that there are only two games either Witcher or Cyberpunk, and all planned games will be based on one of them. Additionally, CD Projekt Red wants to prove that two big games can take us ahead. He added,
"We just want to do a little more games – we'll prove that we can do two big games at the same time, which will take us quite a few years,"
Furthermore, Cyberpunk 2077 multiplayer is next on CD Projekt Red's list after the primary game is released.
Lastly, Kicinski talked about the impact of Coronavirus, saying that they will find a way to reach players.
"The situation regarding coronavirus is so dynamic that it is hard to think about it. We will take part in trade fairs, if they take place. If they do not take place, we will show the same things in a different way – the internet works normally, we have a way to reach players "
We recently got insights on how much playerbase of Witcher Games have been increased after the Netflix show. CD Projekt Red saw massive sale increase in books, games, and Cyberpunk 2077 pre-orders. Their latest game Cyberpunk 2077 release date is delayed to September 17, because further testing of the game was required.
Elon Musk wants to play The Witcher inside Tesla
500,000 New Witcher Books are being printed in the US.
The Witcher 3 player base has increased significantly, on par with Red Dead Redemption now
Witcher 3 playerbase keeps increasing and competes with top multiplayer games.
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EA Play Will Join Xbox Game Pass For PC This December

redpajama_set_name: RedPajamaCommonCrawl


text:
Gli angeli sterminatori (Les anges exterminateurs) è un film del 2006 diretto da Jean-Claude Brisseau.

Trama

Altri progetti

Collegamenti esterni

Film drammatici
Film fantastici

redpajama_set_name: RedPajamaWikipedia


Take my money…I want one.
Wow. Beautiful cabinet outside as well as inside. Nice wiring harness by the way. Gives it a clean, professional look.
As I best recall, they're close enough (though apparently are in some way NOT identical) that it's reasonable for us chronological laity to use one of the contractions to refer to the other :P .
I think UTC was adopted back in the 70s but here in the UK most people have never heard of UTC or DST we use GMT and BST (British Summer Time) instead. Me I just think GMT sounds better cos I have always used it.
How accurate is this time piece? It is showing an impossible/unlikely date – year in the photo for the current refference of time.
Really…? It's not showing the date.
Look again – it says the time is just after 12 and a half minutes past 8 in the evening.
I haven't looked further, but I would expect that a 12-hour clock display is also possible.
You're right. Should have put my glasses on. Sorry about that.
The time is shown as 20hrs 12mins and 34 secs with the date in the UK date format day/month/year 01/01/2015. I took this picture when testing out leap second adjustments. The test program required the actual date of the next leap second.
Very nicely written code, and it's pleasant to see a clock that actually tries to discipline the local oscillator rather than just stepping it. 1 second per day isn't actually "extremely accurate", but I think this code actually does quite a bit better than that unless you have a particularly bad crystal!
(specifically it's the DCF77 library that has very nice code).
Andrew you are correct 1 second per day in not "extremely accurate". The actual clock using the DCF77 library to auto tune will be accurate to within than a second a day over many days without any DCF77 signal. The actual DCF77 signal has never been off for more than 30mins (during a lightning storm) according to the DCF77 site.
The 1s figure is because Brett tested my library for 1 day with 1s resolution. He then found that it was better than his old library. If people would start to read the details on my library they would find that it is ***significantly*** better both with regard to noise tolerance as well as the tuning.
Technically the statement is not wrong though. It's like saying "New York City: population > 200 000".
In technical terms: in the short term the library phase locks to DCF77 with 10 ms resolution. In the long term it frequency locks. Depending on the crystal and especially the temperature it will tune the frequency to something between 1-16 Hz. Right now (after 3 days of retuning after a cold start) I notice that it deviates by ~ 20 ms per day or 1/4 ppm.
Also the noise tolerance is much better than any other open source library. It is also much better than most closed source stuff. Actually it is better than any closed source stuff that I got hold on. I do not know how it competes the top of the line though. I found at least 2 closed source publications that hint at a probably more cabable implementations – but they used an FPGAs and a dedicated tuner including high speed D/A converters. So the hardware is already far superior. For low cost hardware I am pretty much sure that there is nothing that competes with my library.
they had these back in the 50s in America. Many apartment buildings and public buildings(schools/courthouses/etc) still have them. They probably don't run off the closest atomic clock freq though, but it wouldn't be hard to make something that would set all the clocks in those buildings though.
I have a radio clock. It's pretty cool. I don't have to set it for daylight savings because it does it by itself. I figure the time is right on it for the most part too. Mine doesn't set any of my other clocks though.
They're generally very accurate. I just called the USNO talking clock (202-762-1401 or 719-567-6742) and it exactly matches the radio clock on my desk and the one on the wall.
The clocks main displays always correct for daylight saving time and leap seconds but the 30 second slave clocks need to be manually started and will then will auto adjust. This is because they are quite noisy in operation and I not not want to have clocks pulsing 120 times in the middle of the night.
I think that the "1 second per day" deviation could be improved.
Using the DCF77 library to auto tune will the clocks will be accurate to within than a second a day over many days without any DCF77 signal. With any sort of DCF77 signal noisy or otherwise they are 100% correct.


Three local Detroit tech startups are one step closer to pitching their products at the annual Google Demo Day in Silicon Valley hosted in April. Local Ann Arbor companies AdAdapted Inc., Genomenon and Detroit-based LevelEleven won the top three spots at the Grand Circus pitch competition, and will be the top contenders from the area for the Google Demo Day opportunity. The three weren't ranked in any order, and judges say that one or possibly two of the companies could move forward and onto the Google pitch phase.
In a previous year, Detroit company iRule LLC. was chosen to pitch at the Silicon Valley event. They didn't win funding from that particular contest, but went on to raise a venture capital round of $2.5 million later that year.
This year's contenders are hoping to outshine the competition with their innovative technologies. AdAdapted builds simple ads designs for mobile devices, while Genomenon is a software that optimized human genome data sequencing for researchers and geneticists. LevelEleven is a portfolio company of Detroit Venture Partners that makes sale-motivation optimized software. Each of the companies has seen success in the local market, and aspires towards venture capital funding and continual growth.
Detroit tech companies are surely representing in their local market and Wheel Media is excited to see these new companies gain traction and funding in Silicon Valley this April.


Location : Watching over my eggs.
Savvy! You don't know me by now?
Eh. For lack of originalness, Savvy again.
Nope. I have dropped in for a bit.
Wow, we haven't played this one in a while.
I'm sorry, but you are incorrect.
OBJECTION!!! THAT IS IMPOSSIBLE BECAUSE SAV IS GOING TO POST NEXT!!!
Absolutely. This game is too easy with only two people on.


Jurassic World Alive is now available for IOS and Android users. It is the best part of it to see real-world locations just like Pokemon Go. In the game, a lots of dinosaurs are available like real world for you. It's hard to play for the beginners, but try to watch all tutorials properly to understand it. Try to play it because it is a very good source to reduce the stress by playing it. You have to learn many more things to play it.
You can't pick the dinosaurs directly because; in Jurassic World Alive you have to shoot the dinosaur with darts to extract all DNA samples. Players can easily catch the dinosaurs by making a clone. For finding dinosaurs, you have to open the map to know the location of them. If you want to shoot the darts, then you should place your finger on the screen and try to move it around the target. Take help from drone to make a target. If you want to create the dinosaur, then you have to collect the DNA samples of same species. After collecting these all things, you should click on the create button to finish the process of making the dinosaur.
This game has an offer wall for you to collect the free offers or free rewards without any mission. If you want to get great offers here, then you should download other small games. Just keep browsing for getting the best offers.
Try to use basic attacks in the time of battles, because you can use the strong move at difficult fights. You can use the strong moves only for the limited time, so it is better for you to don't waste them unnecessarily by using Jurassic World Alive Hack.


text:
Pennsylvania Governor Announces School Safety Task Force
By Jim Melwert March 15, 2018 at 3:48 pm
Filed Under:Jim Melwert, Pennsylvania, Tom Wolf
LOWER MERION, PA (CBS) — One day after students across the country walked out of class in protest over inaction following the Parkland school shooting, Pennsylvania Gov. Tom Wolf was in Lower Merion at Penn Wynne Elementary School to announce the creation of a school safety task force.
Wolf says the task force will focus on three areas: mental health treatment and needs, building safety, and the response by teachers and staff in the case of a violent incident.
"We need to listen to teachers, we need to listen to law enforcement officials, we need to listen to parents, we need to listen to the students, and that's what this task force is about," said Wolf.
Wolf says the panel will include former Philadelphia Police Commissioner Charles Ramsey, the director of the Pennsylvania Association of School Administrators, and the president of the Pennsylvania Parent Teacher Association.
"It's a balance that we've got to strike, we can't let the focus on safety completely ruin the spontaneity of the lives of our children. On the other hand, safety is really important, you can't be spontaneous if you're not alive," Wolf said.
Wolf says meetings will be held throughout the state.


Wedges of fossilized Palmwood Agate striking stones swing from two pyrope garnet faceted bezel set stones. A textured wedge of sterling silver is accented with 18k yellow gold lichen half moon shapes. A lichen oval playfully juts out from the side of the agate bezel.
The earrings are oxidized to a smoke color that has been carefully textured with a soft brushed finish. They earrings hang at 1.75" from the post. The agates are 1" by .33" wide.


Suppose, you have an ASP.NET Web Application and a class library with a Telerik Data Access fluent model.
Add a new master page to the web application project (SofiaCarRentalWebApp). Name it Site.Master.
Open the Master Page for your web application.


The Cranbrook Hotel is ideally situated for both businesses and recreational purposes. The pleasant location allows for easy access to central London (15 min, via British rail) and five minute from Gants Hill Tube station. A short walk into the centre of town provides the visitor with a number of essential facilities with newly established shopping precincts, markets and all major banks & building societies. However, if relaxation is on your agenda then a number of recreational pursuits are close at hand. For the thespian there is the Kenneth Moore Theatre which attracts many well known dance and drama acts while for the more active a stroll through beautiful Valentines Park can be just the thing to provide an appetite for the evenings entertainment. For the night owls there are a large variety of pubs, clubs and restaurants to choose between as well as snooker clubs, top golf courses and a leisure centre for those that like to keep in shape.
This charming, quietly located boutique hotel is situated within walking distance of the famous designer shops and continental cafe bars and restaurants of Hampstead Village, St John's High Street, Primrose Hill and Belsize Park. Swiss Cottage, Regent's Park, Primrose Hill Park, London Zoo and Lord's Cricket Ground are all within easy reach. With so much to do and see you may never need to move out of the area. If you do, the West End theatres and London's many attractions are easily accessible, with Chalk Farm, Belsize Park and Swiss Cottage underground close by. These provide easy access to main line railway stations, King's Cross and St. Pancras, Waterloo/Europe via the Euro star terminal and the Paddington/Heathrow and Victoria/Gatwick express trains that run frequently. The hotel has a pleasant restaurant and bar with an outside terrace. There is limited parking under the hotel and on street parking between 6pm and 9am.
A modern hotel with 99 ensuite bedrooms is an ideal base for both corporate and leisure clients alike. Situated on the southern tip of Epping Forest , the County Hotel is the perfect base for visiting the sights, theatres and shopping of London. It offers good motorway links, with tube and rail services providing only a 30-minute journey to Central London. Lakeside Shopping Centre and Waltham Abbey are also within easy reach. The relaxing location makes the hotel an ideal venue for seminars, training courses and private functions, but with the benefit of excellent accessibility. The hotel also holds a licence for civil wedding ceremonies.
En spansk-amerikanska boutiquehotell, genomsyrad av sjudande kubanska färgtoner, handmålad Mexicasa kakel, kolonialistiska möbler och kyrkokonst. Church Street Hotel ligger i Camberwell, erbjuda en verklig downtown London livserfarenhet medan det fortfarande är inom räckhåll av Londons turistattraktioner. Energisk lokala konstscenen, sprudlande nattliv, och rikedom av lokala matställen i närheten. Musikställen i Brixton och Vauxhall ca 10 min med buss, medan stora turistattraktioner såsom Buckingham Palace, London Eye, Tate Gallery, Houses of Parliament, Oxford street. etc ca 20-30 min med buss. Frekvent busslinjer utanför hotellet. Även internationellt erkända Kings College Hospital / Institute of Psychiatry är en kort 10 minuters promenad bort. Detta hotell är absolut en RÖKFRIA EGENDOM.
Kingston Lodge, en Brook Hotel, 7 miles från Londons West End och inom bekvämt avstånd från London Heathrow Airport, M25, M3 och M4. Det finns en över grund-anslutning från Norbiton Station som är kort promenad bort. Richmond Park med flockar av dovhjort är vacker vid alla tider på året, särskilt vid solnedgången och bara 2 minuters promenad från hotellet. De fashionabla centrum av Kingston Upon Thames är en kort promenad med sina utbud av butiker, kaféer och barer. Kingston Lodge Hotel är idealiskt beläget för besök till sevärdheterna i västra London och Surrey, inklusive Twickenham Rugby Ground, Wimbledon, den nya Wembley Stadium, Kew Gardens och Hampton Court Palace. Många av våra 63 välutrustade med egna badrum har egen balkong och utsikt över den fridfulla anlagda innergård. Omfattande, möte, evenemang och konferenslokaler gör Kingston Lodge Hotel den perfekta platsen i Surrey för din konferens, bröllop och andra speciella tillfällen. Trådlöst bredband finns tillgängligt i hela hotellet..
Wembley Plaza Hotel är ett Hilton tillhörande hotell som drivs av Hilton Hotels, därför gästerna bör vara medveten om de möjligheter som kan skilja sig från vad som kan hittas i andra Hilton Hotell i Storbritannien. Hotellet är idealiskt beläget intill Wembley Stadium och Areana , hem till några av Storbritanniens mest populära evenemang. Lätt tillgång till London, med restid mindre än 15 minuter till Baker Street med Londons tunnelbana, vilket gör att West End och andra London-attraktioner inom nära räckhåll. Utformad för din komfort och bekvämlighet, gästrum är elegant inredda och utrustade med alla moderna bekvämligheter som någon resenär kan förvänta sig av ett hotell av denna kategori.
Den Croydon Park Hotel har länge varit känt för det varma välkomnande och mycket hög standard. East Croydon Station 2 minuters promenad från hotellet. Centrala London ligger bara 15 minuter bort med tåg och centrala Croydon har en rikedom av fantastisk underhållning i världsklass affärsfaciliteter. Hotel har ett barnvakter, tvätt och kemtvätt, vårdcentral, ett businesscenter, valutaväxling, uttrycka kassan, parkering beroende på tillgänglighet, en lounge, bar, fantastisk restaurang, 24 timmars rumservice och en superb Hälsa och fritidsanläggning.
The Broadway Lodge is Awarded Two Stars in recognition of the outstanding quality and service we provide to our customers. Next to Tolworth Tower, and M&S car park,we are a well established and respected B&B within the area, and close to all amenities. All rooms are En-Suite and feature: colour TV and Video, Tea & coffee maker and fridge. All prices inc: Full english & continental breakfast. The Broadway Lodge provides very comfortable and welcoming facilities where a friendly and relaxing atmosphere is encouraged. Within easy reach for many local businesses and shopping centers. Transport Links: Tolworth railway stn - m Heathrow airport - 15m, The A3 linking to M25 to Gatwick & Heathrow airport,.Places To go: Chessington World Of Adventure -3m, Wimbledon Tennis Club- 6m, Hampton Court Place-5m, Sandown Park Race course-4m, Kempton Park Race Course-8m, Nearest Towns/nightlife: Surbiton-2m, Kingston Upon Thames-3m, Central London-14m.
The Euro Hotel Clapham is an attractive property overlooking Clapham Common Park. The hotel is located close to the London Underground, with all public transport facilities. The Euro Hotel Clapham is situated on Clapham Common South Side. The hotel offers 44 bedrooms. All rooms are en suite with facilities such as TV, tea/coffee making facilities, hairdryer and trouser press. Some rooms have a balcony overlooking Clapham Common. Public transport is in walking distance of the hotel. Please note that there is no lift at the hotel.
Forest House is a hostel in North London consisting of 21 rooms with shared facilities. All rooms are private. Accommodation includes singles, twins, triples and rooms for 4 persons. Whole building is divided in to 6 comprising units of three- four rooms, two showers and toilets with 24-hour hot water. Upon arrival, guests are given keys to the Front Door (£15 deposit for the keys you will get back after check out), so that they can come and go as they please during their stay. Hostel is situated close to the main travel links and local amenities. Next door restaurants will offer delicious and not expensive meals. You will find quiet pubs, if you are tired from busy London, or welcoming nightclubs and discos within walking distance. Our location comprises busy London and its quiet surroundings. Please note, for the month of December the balance due to the accommodation provider is due and payable immediately after the reservation.
Ursprungligen byggt 1850 för Byron familjen har herrgården är smakfullt restaurerats till sin forna glans. Du kommer nu upptäcka ett vackert hus på landet, som behåller många ursprungliga kännetecken, med en modern glas atrium som ger en ljus och luftig känsla till hotellet. Alla de 35 välutrustade rum har eget badrum, många med utsikt över den imponerande golfbanan. Ta en time out för dig själv och spela en runda golf på vår utmanande 18 golfbanan hål eller dra fördel av vår relaxavdelning där du kan koppla i bastun och ångbastun, spela squash eller tennis och träna i gymmet eller aerobics dansstudio. Coulsdon Manor Hotel & Golf Club är synonymt med gäster som söker lugn och ro. Ställ bland 140 hektar anlagda parklandskap i hjärtat av Surrey, och ändå bara 15 miles från centrala London. Herrgården Restaurangen har ett avundsvärt rykte för förstklassig mat och fina viner och vår Terrace Bar är bara platsen att varva ned och njuta av en uppvärmning och avkopplande atmosfär.
Queens Hotel is a 2-star superior hotel with 105 ensuite bedrooms. The bright reception area at the Queens Hotel leads to a comfortable lounge in which guests can relax, watch television and welcome friends. The warm and friendly bar area is perfect to enjoy a pre or post dinner drink, tea, coffee and snacks. All bedrooms are reached by lift and are individually and comfortably furnished to provide the best of todays amenities, with ensuite facilities, central heating, colour television, trouser press, hair dryer and tea & coffee making facilities. Additionally the hotel has stunning suites.
Raglan Hotel offers a friendly business and family base conveniently located on Queens Avenue, a quiet residential road in the heart of North Londons cosmopolitan Muswell Hill. Just off the bustling Broadway, with easy access to the A1, North Circular and other major road transport arteries: bus and tube links provide quick and convenient access to the West End and City. From spacious family rooms to well-proportioned doubles and characterful singles all 43 bedrooms are large and comfortable. Free movies and broadband internet available in every room, iron/iron board (on request). Sophisticated air-conditioned conference facilities for up to 200 delegates at competitive rates. THE HOTEL HAS LATELY REFURBISHED SOME OF THE ROOMS WITH A SIMPLE AND COMTEMPORARY TASTE. A GARDEN IS ALSO AVAILABLE, A HIDDEN CORNER WHERE OUR GUESTS CAN RELAX AND ENJOY THEIR DRINK.
Welcome to Express by Holiday Inn London-Stratford. All our rooms are air-conditioned and equipped with Qdos in-house entertainment system and High Speed Internet, both wired and wireless. The Express by Holiday Inn London Stratford is situated in the heart of East London, site of the 2012 LONDON OLYMPICS . Stratford Regional & International Station, home to Eurostar rail link , due to be completed in 2009 is a 5 minute walk from Express by Holiday Inn London Stratford . From the terminal, visitors can take Jubilee or Central London Underground line or DLR and travel to O2 Arena , Excel Exhibition Centre , Canary Wharf , London's financial district and West End of London. National Express Trains also operate from this station, for guests travelling towards Essex and East Anglia. With excellent transport links Express by Holiday Inn London Stratford is ideal for business or pleasure. The hotel bar is open daily from 11am to 1.00am to residents and their guests. Complimentary Continental Breakfast is served every morning in our Great Room. Evening meals are served Monday-Friday 18:00-22.00.
The York Hotel is situated in a quiet street in the heart of Ilford Town Centre. Ilford Station is one min walk from the Hotel. There is a beautiful Shopping Mall (The Exchange) where you can enjoy all sorts of food and also can do any kind of shopping. York Hotel offers: • A relaxing atmosphere you'd expect from a privately run family hotel. • Single & Double rooms, some with en-suite bathrooms. • Colour television with satellite. • Tea/Coffee making facilities. • Smoking & non Smoking rooms available. • Car parking at rear for guests. • Convenient for Ilford Station. (15-20) mins. to Liverpool Street Station. Ideal for London, City, West End and Docklands. Our policy is that you arrive as a Guest & you depart as a friend.
Ett nybyggt modernt hotell. Ligger på stranden av Grand Union Canal. Helt på eget sovrum med amneties att passa dina behov. Trådlöst internet i hela byggnaden och trådbundna LAN i sovrum. Utvalda rum har utsikt över kanalen.
Conifers Guest House is located in a tranquil road in the fashionable area of Ilford, close to all the amenities, shops, station and all bus routes. The beautiful Valentines Park is only two minutes away, so enabling you to enjoy a refreshing walk before breakfast. Everything you would expect to find in all well appointed Guest Houses is at Conifers. "Arrive as a guest and leave as a friend." We offer special discounts for large Group Bookings and also special discounted rates at weekends. We are a Registered Hotel. There are 23 beautifully Decorated Letting Rooms in Conifers Guest House.
Visiting London? A newly built hotel suitable for Business travellers to North London, Harrow, Wembley and tourists visiting London. All rooms equipped with television, broadband connection and tea/coffee making facility. Ironing facility and hairdryer available. Continental Breakfast served. Full English breakfast on demand. Spacious, bright and airy dining room. Off-street car parking. Garden. Brent X Hotel, in north west London, is a truly strategic hub for travellers. It's close enough to central London to make a full day's visit comfortable, yet far enough to cut out the grind through traffic when you want to get away. The new WEMBLEY NATIONAL STADIUM, which opened in 2007, is just 2 miles away!! All major credit cards accepted and company accounts welcome The location of the hotel is at a walking distance from Preston Road and Kingsbury Roundabout, which offer facilities of Cafes, Restaurant, and Health Clubs besides all types of shopping facilities. Facilities •	Superior, spacious and comfortable en-suite rooms at very affordable prices •	Tea & Coffee Facility in all rooms •	Full English Breakfast •	Parking available •	TV in rooms •	Ironing & hairdryer facility •	Taxis can be arranged for local transport or Airport pick ups and drop offs. •	Wembley Stadium – 1 mile •	Wireless Broadband Facility Professional staff at hand to help you at all times.
WELCOME The Express by Holiday Inn Park Royal London is ideal for business and leisure travelers, offering the convenience of location to London Heathrow Airport and Central London. Access to London's public transport system is readily available with North Acton Underground Station less than 2 minutes walk from the hotel and 15 minutes into Central London. The hotel is particularly well situated for the Wembley Conference Centre, Kew Gardens, and Ealing Studios. The hotel bedrooms offer all the conveniences required of today's business traveler and holidaymaker. All rooms have air cooling system, and have en-suite bathrooms with power shower, hairdryers, pay-movies, satellite television and high speed wireless internet access.The Hotel is non Smoking establishment from 01st Sept 2008. Only Non-Smoking rooms will be available in the hotel. We have well equipped conference rooms to seat up to 50 persons theatre style. All meeting rooms have natural daylight, air-conditioning and temperature control, modem points, adjustable lighting and telephone-conferencing facilities. Our rates are inclusive of a complimentary continental breakfast buffet. There is also limited on-site parking which is chargeable. If you are visiting London and need convenient access to Central London and London Heathrow Airport, then make the Express by Holiday Inn Park Royal your hotel of choice.
Belgrave Hotel is a bright, modern hotel offering a refreshingly homely atmosphere and great value for money in a Central London location. Our friendly staff will be happy to assist you during your stay with us. We are located within walking distance of Oval underground station providing quick and easy access to anywhere on the underground. London's great attractions are easily accessible within a 10-15 minute journey. We offer a wide range of services to you. All our newly furnished, well-equipped rooms include, en-suite bathroom, LCD TV screen with Sky and room safes. We also have wireless Internet access available throughout the hotel. Parking is available for guests - please contact the hotel prior to your visit to arrange this.
A warm welcome awaits you at the Croydon Court Hotel. This is a spacious yet comfortable hotel offering modern facilities within an elegant Victorian building. We are conveniently located close to the shopping and business hub of nearby central Croydon and are within easy reach of central London. We have secure parking facilities and excellent access to all surrounding areas. Please be aware that the hotel has no lift.
The Best Western Lodge Hotel is located in Putney in South West London. The hotel boasts 60 en-suite hand furnished bedrooms, The Conservatory Restaurant offering a relaxing breakfast, The Sportsman's Bar with a charming lounge area and a large car park and garden. If you are looking for hotels near Wimbledon, or in south west central London, then the Best Western Lodge Hotel in Putney is the perfect place. This privately owned hotel of character is a rare find in Putney, so close to central London and plays host to a large number of weddings and private banquets and corporate seminars and events. Facilities include 3 conference and banqueting suites. Free Wi-Fi is available for guests throughout the hotel. Best Western Lodge Hotel guests can benefit from using the facilities at The Virgin Active Health Club in Putney for just £7.50 per person per day. The Virgin Active Health Club is located 5 minutes' walk from the hotel and offers first class facilities including, state of the art gym and cardio equipment, steam rooms and saunas.
The Norfolk House Hotel is an attractive property conveniently located near the A23, leading to major link roads. The May Day Hospital is just opposite the hotel and Croydon City Centre and Central London are in easy reach by car or Public transport. Norfolk House Conference Rooms is well suited for business class people who wish to accomodate and have their general meetings held at the place of stay. The Norfolk House features a popular Coffee Shop and Function facilities for up to 100 guests. All our en-suite rooms are well appointed and designed with your comfort in mind. Modern Facilities, friendly Service and Value for Money combined, offers a reassuring stay at the Norfolk House Hotel.
Ett land hus hotell som ligger i 205 tunnland parklandskap idealisk för företag sportaktiviteter och team evenemang byggnaden. 26 konferensrum med en maximal möte kapacitet på 350 delegater. Corporate sportaktiviteter och team evenemang byggnad tillgodoses lätt för i motiveringen till hotellet. AA Rosette prisbelönt Cedar Restaurang och Phoenix Bar & Lounge för lättare måltider. En enastående tillgång för Selsdon Park Hotel är 18 håls golfbana i mästerskapsklass som anges i 1929 med fem gånger British Open Golf Champion, JH Taylor. Ursprungligen skära i tjock skog som kläder denna del av Surrey Hills, kurs har utökats under åren, men layouten är fortfarande väsentligt som det var sextio år sedan. Anm: Alla rum och allmänna utrymmen Selsdon Park Hotel & Golf Club är nu icke-rökare. Hotellet har dock rökområden ut sidan byggnaden. Dessutom har hotellet inte längre rumsservice. Gäster som inte vill äta i restaurangen kan få en lätt måltid i Tempus Bar.
The Park Hotel is located opposite Valentine's Park, which is beautiful and well cared for, covering over 136 acres. There are amenities for tennis, mini-golf, swimming and boating. The park is well known for its old English garden, the Rosary Terrace and its lakes. The hotel is ideally placed for all public transport – a few minutes walk from Central London Line Underground, Gants Hill, and British Rail, Ilford stations (twenty minutes to Central London). It is, therefore, an ideal situation for visiting London to see the sights. Eight bus routes pass the door to Barking, Dagenham, Chigwell and further afield. Park Hotel has been established for over twenty-five years and is run as a family business by the resident proprietors. Free car parking is available in the hotel grounds.
A modern hotel with 138 en-suite bedrooms all furnished to the highest standard, the Palms Hotel is easily accessible to all types of traveller.It features a Restaurant and bar, both with extensive menus, has 13 conference rooms and can cater for functions for up to 300 people.


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Julian Ahmataj (24 de Maio de 1979, Albânia) é um futebolista albanês que joga como meio-campista atualmente pelo Flamurtari Vlorë da Albânia .

Futebolistas da Albânia
Naturais de Elbasan
Futebolistas do KS Elbasani
Futebolistas do KS Bylis Ballsh
Futebolistas do FK Dinamo Tirana
Futebolistas do Teuta Durrës
Futebolistas do KF Tirana
Futebolistas do Flamurtari Vlorë


International container ships station themselves in the Burrard Inlet and English Bay off the shores of Vancouver, British Columbia, Canada while awaiting docking time in the city's port. English Bay lies west of the downtown Vancouver peninsula, which separates the bay from Burrard Inlet connecting to the northwest, and False Creek to the southeast. Point Grey on the south and Point Atkinson on the north form the bay's entrance from the Strait of Georgia.
These Cargo ships are just as much as part of the landscape as the West and North Shore mountains of BC. They are in every sunset and sunrise in Vancouver this has been our backyard for years and we've been fortunate to captured many surreal scenic photographs. Every photograph in this gallery has been geotagged, click the info icon on the bottom right to discover exact location of the capture.


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You are here: Fashion Wiki » Janis Ancens
janis-ancens
Janis Ancens
Janis Ancens is a Latvian male model that has walked for J. Crew, Fendi and Ermenegildo Zegna.
Height: 6' 1.5"
Chest: 38"
Hair Color: Dark Blonde
Janis Ancens is signed with Bon Image Corp Tokyo, Scoop Models Copenhagen, Elite Barcelona, Elite London, Elite Milan, Elite Paris and Wilhelmina New York.
In 2012 Janis Ancens walked the fall shows for Valentino, Moschino, Burberry, Gucci, Yves Saint Laurent, Bottega Veneta, Tommy Hilfiger, Marc by Marc Jacobs and Costume National. He walked the spring shows for Topman, Valentino, Jil Sander, Burberry, Neil Barrett, Bottega Veneta, Salvatore Ferragamo, Prada, Ermenegildo Zegna, Gucci, Raf Simons, Louis Vuitton, Kris Van Assche, Dior Homme, Tommy Hilfiger, Just Cavalli and Costume National. He was on the November cover of 10 Men Magazine. He appeared in an editorial for the spring issue of Man About Town that was called Let Sleeping Dogs Lie. He appeared in an editorial for the fall issue of L'Officiel Hommes Italia that was called Stile Italiano. He appeared in an editorial for the fall issue of 10 Men Magazine that was called The Model: Janis. He appeared in an editorial for the fall issue of GQ Style Italy that was called Mr Fantasy. He appeared in an editorial for the November issue of H&M. he was in the fall ad campaigns for Calvin Klein Jeans, Belstaff, Neil Barrett and Dior Homme. He was in the resort ad campaign for Gucci.
In 2013 he walked the fall shows for Topman, Joseph Abboud, Jil Sander, Ermenegildo Zegna, Burberry, Les Hommes, Neil Barrett, Bottega Veneta, Salvatore Ferragamo, Prada, Gucci, Belstaff, Valentino, Raf Simons, Kris Van Assche, Dior Homme, Wooyoungmi, Damir Doma, Paul Smith, Acne Studios and Calvin Klein. He walked the spring shows for Burberry, Ermenegildo Zegna, Jil Sander, Neil Barrett, Bottega Veneta, Trussardi, Iceberg, Moncler, Gucci, Belstaff, Valentino, Wooyoungmi, Louis Vuitton, Dries Van Noten, Dior Homme, Damir Doma, Thom Browne, Y-3 and Tim Coppens. He appeared in an editorial for the March issue of Zoo Magazine that was called Janis Ancens. He appeared in an editorial for the March issue of Panorama Icon Magazine that was called Villa Necchi. He appeared in an editorial for the spring issue of Man About Town that was called Round Midnight. He appeared in an editorial for the spring issue of Man About Town that was called Diplomat. He appeared in an editorial for the May issue of Interview Magazine that was called On the Beach. He appeared in an editorial for the June issue of MFF Magazine that was called Black VS White. He appeared in an editorial for the July issue of MFF Magazine that was called Murder, He Wrote. He appeared in an editorial for the September issue of V Man that was called Through the Years. He appeared in an editorial for the fall issue of Hercules Magazine that was called All the Right Notes. He appeared in an editorial for the fall issue of M Magazine that was called Inner Sense. He appeared in an editorial for the fall issue of Numero Homme that was called Le Culte de Secret. He appeared in an editorial for the November issue of Details Magazine that was called Black Out. He appeared in an editorial for the December issue of Candy Magazine that was called Uniformen and Drama. He was in the spring ad campaigns for J Brand, Belstaff, Burberry Japan and Dior Homme. He was in the spring ad campaign for Sandro.
In 2014 he walked the fall shows for Costume National, Jil Sander, Neil Barrett, Bottega Veneta, Iceberg, Philipp Plein, Gucci, Ermenegildo Zegna, Belstaff and Valentino. He walked the spring shows for Jimmy Choo, Ermenegildo Zegna, Costume National, MSGM, Neil Barrett, Les Hommes, Philipp Plein, Bottega Veneta, Gucci, Tillmann Lauterbach, Belstaff, Roberto Cavalli and Valentino. He was on the June cover of Essential Homme. He was on the fall cover of The Greatest Magazine. He appeared in an editorial for the spring issue of Dazed Magazine that was called Dior Homme. He appeared in an editorial for the spring issue of V Man that was called Higher Learning. He appeared in an editorial for the April issue of The Room that was called Travelling Without Moving. He appeared in an editorial for the June issue of Essential Homme that was called Far & Away. He appeared in an editorial for the July issue of unFLOP Magazine that was called Agastopic. He appeared in an editorial for the fall issue of The Greatest Magazine that was called Janis Ancens. He appeared in an editorial for the fall issue of UnTitled Project that was called Ciao Maschiof. He was in the spring ad campaigns for Valentino, Kris Van Assche, Tommy Hilfiger, Zara and Pull & Bear. He was in the fall ad campaigns for Gucci, Valentino and Bottega Veneta.
In 2015 he walked the fall shows for Sandro, Coach, Dunhill, Ermenegildo Zegna, Les Hommes, Neil Barrett, Philipp Plein, Bottega Veneta, Moncler, Fendi, Diesel Black Gold, Canali, Roberto Cavalli, Valentino, Dries van Noten, Timo Weiland, Perry Ellis, Billy Reid, J. Crew and Mango. He walked the spring shows for Coach, Belstaff, Ports 1961, Corneliani, Les Hommes, Philipp Plein, Bottega Veneta, Moncler, Canali, Fendi, Valentino, Dries Van Noten and Berluti. He was on the May cover of Manifesto Magazine. He was on the September cover of Details Magazine. He appeared in an editorial for the April issue of D Magazine that was called Il Grande Blu. He appeared in an editorial for the May issue of Manifesto Magazine that was called On The Up and Up. He appeared in an editorial for the July issue of Essential Homme that was called The Way It Falls. He appeared in an editorial for the July issue of Sportswear International that was called Alter Skater. He appeared in an editorial for the September issue of Details Magazine that was called Role Models. He appeared in an editorial for the September issue of MFF Magazine that was called Valentino Unplugged. He was in the spring ad campaign for Weston. He was in the fall ad campaigns for Club Monaco and Roy Rogers.
In 2016 he walked the fall shows for Adidas, Ermenegildo Zegna, Les Hommes, Pal Zileri, Moncler, Canali, Fendi, J. Crew and Thom Browne. He walked the Jeffrey Fashion Cares Show. He was in the spring ad campaigns for River Island and Uniqlo. His look books include the 2012 look books for Dior Homme, Belstaff and Louis Vuitton, the 2013 look books for Balmain, Prada and Gucci, the 2014 look books for Armani Exchange, Pull & Bear, Dunhill and Ermenegildo Zegna and the 2015 look books for Ermenegildo Zegna, Gucci and Zara.
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janis-ancens.txt · Last modified: Oct 11, 2017 13:31 (external edit)