Answers.Body: <p>Indeed originally Rosenblatt's perceptron's input was conceived to be binary only from biological consideration, but in modern sense <a href="https://en.wikipedia.org/wiki/Perceptron#Definition" rel="nofollow noreferrer">perceptron</a> can handle real-valued inputs as well.</p>
  <blockquote>
  <p>In the modern sense, the perceptron is an algorithm for learning a <em>binary classifier</em> called a threshold function: a function that maps its input <span class="math-container">${\displaystyle \mathbf {x} }$</span> (a real-valued vector) to an output value <span class="math-container">${\displaystyle f(\mathbf {x} )}$</span> (a single binary value)</p>
  </blockquote>
  <p>Therefore it can handle both binary and real-valued inputs, making it versatile for various classification tasks.</p>
  
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Answers.Text: Quoting Wikipedia is not really the best idea. Wikipedia can be written by anyone, including people that have no idea what they are talking about. It's better to quote reliable books or research papers, if you really want to quote.
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Answers.Text: @nbro looks quite like the articles at w3schools  ... . They at least should have noted that there is something odd comparing their definition with the method of training they present. May I and ask for a hint to a *solid* introduction? I'm tired of that buzzword bingo that keeps showing up when I look for AI tutorials on the net.
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Body: <p>What is the definition of a perceptron?</p>
  <p>OK, you are right, this question <em><strong>looks</strong></em> like lacking effort; it doesn't.</p>
  <p>On the net, I have found many definitions that restrict the input of a perceptron to <span class="math-container">$n$</span> <em><strong>binary</strong></em> inputs. If that was really true, there would be many possible ways to simplify a perceptron, obviously to replace it with a truth-table, with good gain on performance.</p>
  <p><a href="https://www.w3schools.com/ai/ai_perceptrons.asp" rel="nofollow noreferrer">https://www.w3schools.com/ai/ai_perceptrons.asp</a> tells me inputs are 0 or 1.</p>
  <p>I have seen other sites stating the input would be binary; then, I was in good faith-mode and didn't collect the links.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-08T20:21:06.697Z
Comments.Text: For a more modern treatment, see Elements of Statistical Learning by Hastie et al. The perceptron discussed in this book allows for any real valued vector input. In modern literature, the perceptron is used as building block for the feedforward neural network, AKA multilayer perceptron models.
Comments.UserDisplayName: hff1
Tags: <perceptron>
Title: What is the definition of a perceptron?
UserDisplayName: Gyro Gearloose
ThreadText: # What is the definition of a perceptron?
  
  (Asked by: username_0 on 2024-08-04)
  
  What is the definition of a perceptron?
  
  OK, you are right, this question ***looks*** like lacking effort; it doesn't.
  
  On the net, I have found many definitions that restrict the input of a perceptron to $n$ ***binary*** inputs. If that was really true, there would be many possible ways to simplify a perceptron, obviously to replace it with a truth-table, with good gain on performance.
  
  <https://www.w3schools.com/ai/ai_perceptrons.asp> tells me inputs are 0 or 1.
  
  I have seen other sites stating the input would be binary; then, I was in good faith-mode and didn't collect the links.
  
  **Tags:** perceptron
  
  > **Comments:**
  > **username_1**: For a more modern treatment, see Elements of Statistical Learning by Hastie et al. The perceptron discussed in this book allows for any real valued vector input. In modern literature, the perceptron is used as building block for the feedforward neural network, AKA multilayer perceptron models.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2024-08-04. Score: 0 | Accepted answer
  
  Indeed originally Rosenblatt's perceptron's input was conceived to be binary only from biological consideration, but in modern sense [perceptron](https://en.wikipedia.org/wiki/Perceptron#Definition) can handle real-valued inputs as well.
  
  > In the modern sense, the perceptron is an algorithm for learning a *binary classifier* called a threshold function: a function that maps its input ${\displaystyle \mathbf {x} }$ (a real-valued vector) to an output value ${\displaystyle f(\mathbf {x} )}$ (a single binary value)
  
  Therefore it can handle both binary and real-valued inputs, making it versatile for various classification tasks.
  
  > **Comments:**
  > **username_3**: Quoting Wikipedia is not really the best idea. Wikipedia can be written by anyone, including people that have no idea what they are talking about. It's better to quote reliable books or research papers, if you really want to quote.  
  > **username_0**: @username_3 looks quite like the articles at w3schools  ... . They at least should have noted that there is something odd comparing their definition with the method of training they present. May I and ask for a hint to a *solid* introduction? I'm tired of that buzzword bingo that keeps showing up when I look for AI tutorials on the net.  
  
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