<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>

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2020-10-21T09:07:29.660Z
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.
pasaba por aqui
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2020-10-21T09:07:57.030Z
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.
pasaba por aqui
CC BY-SA 4.0
2020-10-21T09:39:29.333Z
But why Bipolar representation works better for this problem?
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<neural-networks><deep-learning><backpropagation>
XOR problem with bipolar representation
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# 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?  

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## 0 answers

