Answers.Body: <p>I found the answer to my question. </p>
  
  <p>Treat <span class="math-container">$\theta$</span> as a normal weight, associated with an input that always equals <span class="math-container">$-1$</span>.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-10-06T23:11:16.973Z
Answers.LastActivityDate: 2020-03-10T22:42:04.723Z
Answers.LastEditDate: 2020-03-10T22:42:04.723Z
Answers.UserDisplayName: Mr. Eivind
Body: <p>I am using the following perceptron formula <span class="math-container">$\text{step}\left(\sum(w_ix_i)-\theta \right)$</span>.</p>
  
  <p>Is <span class="math-container">$\theta$</span> supposed to be updated in a perceptron, like the weights <span class="math-container">$w_i$</span>? If so, what is the formula for this?</p>
  
  <p>I'm trying to make the perceptron learn AND and OR, but without updating <span class="math-container">$\theta$</span>, I don't feel like it's possible to learn the case where both inputs are <span class="math-container">$0$</span>. They will, of course, be independent of the weights, and therefore the output will be <span class="math-container">$\text{step}(-\theta)$</span>, meaning <span class="math-container">$\theta$</span> (which has a random value) alone will determine the output.</p>
  
Tags: <neural-networks><machine-learning><perceptron>
Title: Is the bias supposed to be updated in the perceptron learning algorithm?
UserDisplayName: Mr. Eivind
ThreadText: # Is the bias supposed to be updated in the perceptron learning algorithm?
  
  (Asked by: username_0 on 2018-10-04)
  
  I am using the following perceptron formula $\text{step}\left(\sum(w\_ix\_i)-\theta \right)$.
  
  Is $\theta$ supposed to be updated in a perceptron, like the weights $w\_i$? If so, what is the formula for this?
  
  I'm trying to make the perceptron learn AND and OR, but without updating $\theta$, I don't feel like it's possible to learn the case where both inputs are $0$. They will, of course, be independent of the weights, and therefore the output will be $\text{step}(-\theta)$, meaning $\theta$ (which has a random value) alone will determine the output.
  
  **Tags:** neural-networks, machine-learning, perceptron
  
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  ## 1 answers
  
  ### Answer by username_0 on 2018-10-06. Score: 0 | Accepted answer
  
  I found the answer to my question.
  
  Treat $\theta$ as a normal weight, associated with an input that always equals $-1$.
  
  
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