Body: <p>I am taking a course in Machine Learning and the Professor introduced us to the XOR problem.</p>
  <p>I understand the XOR problem is not linearly separable and we need to employ Neural Network for this problem.</p>
  <p>However, he mentioned XOR works better with Bipolar representation(-1, +1) which I have not really understand.</p>
  <p>I am wondering what Bipolar representation would be better than Binary Representation? Whats the rationale for saying so?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-21T09:07:29.660Z
Comments.Text: If bipolar means values +1 and -1 only, this scheme is totally equivalent to {0, +1}, being the mapping (x+1)/2. The NN can handle both, even doing the mapping at a first layer with bias. Some activation functions could be more valid in one case or another thinking in learning performance.
Comments.UserDisplayName: pasaba por aqui
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-21T09:07:57.030Z
Comments.Text: xor is not separable with a single hyperplane, but it is with two. There are lots of methods different to NN able to handle this subject.
Comments.UserDisplayName: pasaba por aqui
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-21T09:39:29.333Z
Comments.Text: But why Bipolar representation works better for this problem?
Comments.UserDisplayName: Exploring
Tags: <neural-networks><deep-learning><backpropagation>
Title: XOR problem with bipolar representation
UserDisplayName: Exploring
ThreadText: # XOR problem with bipolar representation
  
  (Asked by: username_0 on 2020-10-21)
  
  I am taking a course in Machine Learning and the Professor introduced us to the XOR problem.
  
  I understand the XOR problem is not linearly separable and we need to employ Neural Network for this problem.
  
  However, he mentioned XOR works better with Bipolar representation(-1, +1) which I have not really understand.
  
  I am wondering what Bipolar representation would be better than Binary Representation? Whats the rationale for saying so?
  
  **Tags:** neural-networks, deep-learning, backpropagation
  > **Comments:**
  > **username_1**: If bipolar means values +1 and -1 only, this scheme is totally equivalent to {0, +1}, being the mapping (x+1)/2. The NN can handle both, even doing the mapping at a first layer with bias. Some activation functions could be more valid in one case or another thinking in learning performance.  
  > **username_1**: xor is not separable with a single hyperplane, but it is with two. There are lots of methods different to NN able to handle this subject.  
  > **username_0**: But why Bipolar representation works better for this problem?  
  
  ---
  
  ## 0 answers
  
  