Answers.Body: <p>You can formulate RL and unsupervised learning as &quot;some sort of supervised learning&quot;... in the case of UL you have that the target is an handcrafted task, it being similarity learning, next token prediction and what not, and in case of RL, the reward signal is by definition a form of supervision</p>
  <p>The reason why we distinguish them it's because they aim at solving very different problems, but this is subjective</p>
  <p>There are other types of learning, here are few of them:</p>
  <ul>
  <li>semi-supervised learning</li>
  <li>weakly-supervised learning</li>
  <li>meta learning</li>
  <li>self-supervised learning</li>
  <li>transfer learning</li>
  <li>anomaly detection</li>
  </ul>
  <p>as per the others, you can lead these back to SL UL or RL, becuase at the end of the day, everything can be thought as an Energy model of some sort...</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-02-03T16:34:24.583Z
Answers.Text: I'd would not say that anomaly detection is a type of learning. In fact, you can use unsupervised and supervised methods for anomaly detection. I'd rather say that it's a general task.
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-02-03T19:16:43.730Z
Answers.Text: @nbro anomaly detection includes dataset which contains both valid and unvalid points, thus it's not just density estimation (indeed, it's also known as outlier detection)
Answers.UserDisplayName: Alberto
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-10-11T12:22:48.480Z
Answers.LastActivityDate: 2023-10-11T12:22:48.480Z
Answers.UserDisplayName: Alberto
Body: <p>I read in books, blogs, and articles that there are three learning paradigms: supervised, unsupervised, and reinforcement.</p>
  <p>However, I have never found a proof that this list is exhaustive. Can it be proven that there cannot be other types of learning?</p>
  <p>The notions 'supervised' and 'unsupervised' could be complementary to each other, like if <span class="math-container">$S$</span> is supervised and <span class="math-container">$\bar S$</span> is unsupervised, then <span class="math-container">$S\cap\bar S=\emptyset$</span> and <span class="math-container">$S\cup\bar S=U$</span> where <span class="math-container">$U$</span> is the set of all learning paradigms. However, this would be true only if <span class="math-container">$S$</span> and <span class="math-container">$\bar S$</span> were mutually exclusive and exhaustive. I don't believe that this is the case. First, there is reinforcement learning which is neither <span class="math-container">$S$</span> nor <span class="math-container">$\bar S$</span>. Then, there is nothing that prevents learning to be not fully supervised or not fully unsupervised, e.g. labels can be not well defined, the teacher may teach only partially, but the system can still improve i.e. learn. And if there is reinforcement learning with is neither supervised nor unsupervised, can there be some other learning which is neither supervised, nor unsupervised, nor reinforcement?</p>
  <p>Is it correct to say that in principle there can be many different learning paradigms?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-12T19:09:02.317Z
Comments.Text: Who says that? Who says there are only three paradigms?
Comments.UserDisplayName: Robbie Goodwin
Tags: <terminology><definitions><philosophy><proofs><taxonomy>
Title: Why there are only three machine learning paradigms: supervised, unsupervised, reinforcement?
UserDisplayName: Vladislav Gladkikh
ThreadText: # Why there are only three machine learning paradigms: supervised, unsupervised, reinforcement?
  
  (Asked by: username_0 on 2023-10-11)
  
  I read in books, blogs, and articles that there are three learning paradigms: supervised, unsupervised, and reinforcement.
  
  However, I have never found a proof that this list is exhaustive. Can it be proven that there cannot be other types of learning?
  
  The notions 'supervised' and 'unsupervised' could be complementary to each other, like if $S$ is supervised and $\bar S$ is unsupervised, then $S\cap\bar S=\emptyset$ and $S\cup\bar S=U$ where $U$ is the set of all learning paradigms. However, this would be true only if $S$ and $\bar S$ were mutually exclusive and exhaustive. I don't believe that this is the case. First, there is reinforcement learning which is neither $S$ nor $\bar S$. Then, there is nothing that prevents learning to be not fully supervised or not fully unsupervised, e.g. labels can be not well defined, the teacher may teach only partially, but the system can still improve i.e. learn. And if there is reinforcement learning with is neither supervised nor unsupervised, can there be some other learning which is neither supervised, nor unsupervised, nor reinforcement?
  
  Is it correct to say that in principle there can be many different learning paradigms?
  
  **Tags:** terminology, definitions, philosophy, proofs, taxonomy
  > **Comments:**
  > **username_1**: Who says that? Who says there are only three paradigms?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-10-11. Score: 5 | Accepted answer
  
  You can formulate RL and unsupervised learning as "some sort of supervised learning"... in the case of UL you have that the target is an handcrafted task, it being similarity learning, next token prediction and what not, and in case of RL, the reward signal is by definition a form of supervision
  
  The reason why we distinguish them it's because they aim at solving very different problems, but this is subjective
  
  There are other types of learning, here are few of them:
  
  * semi-supervised learning
  * weakly-supervised learning
  * meta learning
  * self-supervised learning
  * transfer learning
  * anomaly detection
  
  as per the others, you can lead these back to SL UL or RL, becuase at the end of the day, everything can be thought as an Energy model of some sort...
  
  > **Comments:**
  > **username_3**: I'd would not say that anomaly detection is a type of learning. In fact, you can use unsupervised and supervised methods for anomaly detection. I'd rather say that it's a general task.  
  > **username_2**: @nbro anomaly detection includes dataset which contains both valid and unvalid points, thus it's not just density estimation (indeed, it's also known as outlier detection)  
  
  ---
  
  