Answers.Body: <p>The voting technique that you described is called <a href="https://en.wikipedia.org/wiki/Ensemble_learning" rel="nofollow noreferrer">Ensemble Learning</a> and its improvement over time is guaranteed if each classifier is at least a little better than random.</p>
  
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Answers.CreationDate: 2018-01-03T14:13:03.930Z
Answers.LastActivityDate: 2018-01-03T17:22:04.860Z
Answers.LastEditDate: 2018-01-03T17:22:04.860Z
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Body: <p>Suppose I have a classification problem with a stream of training-samples constantly arriving over time. I cannot keep all training-samples in memory, but I still want to train a classifier that will have the "wisdom" of all samples, and additionally, I want the classifier to become better whenever it gets new samples. </p>
  
  <p>I thought of the following idea. Suppose we have enough memory to keep 100 samples. Then, for each run of 100 samples, we will train a different sub-classifier. We will have a meta-classifier that will classify based on voting between all existing sub-classifiers. Over time, we will have more and more sub-classifiers, so hopefully the meta-classifier will improve with time - it will have a "wisdom of the crowds" effect. </p>
  
  <p>Has this method been tried before? Specifically, has it been tried in a deep-learning sequence-classification setting?</p>
  
Tags: <machine-learning><deep-learning><classification>
Title: Creating a classifier for simpler classifiers trained on few training samples
UserDisplayName: Erel Segal-Halevi
ThreadText: # Creating a classifier for simpler classifiers trained on few training samples
  
  (Asked by: username_0 on 2018-01-03)
  
  Suppose I have a classification problem with a stream of training-samples constantly arriving over time. I cannot keep all training-samples in memory, but I still want to train a classifier that will have the "wisdom" of all samples, and additionally, I want the classifier to become better whenever it gets new samples.
  
  I thought of the following idea. Suppose we have enough memory to keep 100 samples. Then, for each run of 100 samples, we will train a different sub-classifier. We will have a meta-classifier that will classify based on voting between all existing sub-classifiers. Over time, we will have more and more sub-classifiers, so hopefully the meta-classifier will improve with time - it will have a "wisdom of the crowds" effect.
  
  Has this method been tried before? Specifically, has it been tried in a deep-learning sequence-classification setting?
  
  **Tags:** machine-learning, deep-learning, classification
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-01-03. Score: 2 | Accepted answer
  
  The voting technique that you described is called [Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning) and its improvement over time is guaranteed if each classifier is at least a little better than random.
  
  
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