Talk:Perceptron
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I hope this is more accurate than the previous version: can someone who knows more than me fact-check this article, and if necessary bring neural networks into sync?
As discussed on Talk:Artificial neuron, there is a lot of overlap between articles on this and various related topics. See that page for some suggestions on how they might best be seperated. - IMSoP 18:29, 11 Dec 2003 (UTC)
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[edit] Is my Pic ok
Please see the discussion page for the XOR perceptron net
[edit] pocket ratchet algorithm
Having never heard of this algorithm, I'm disapointed it doesn't have more discussion, either here or on it's own page.
[edit] McCulloch-Pitts redirect
I'll try and tidy up this article when I get the chance, but McCulloch-Pitts neuron should NOT redirect here. MP neurons are threshold units, whereas neurons in a perceptron model are linear functions of their summed inputs. An MP neuron's activation is essentially u(x) + b, where u(x) is the heaviside step function, x is the sum of inputs (inputs can either be +1 excitatory or -1 inhibitory), and b is a bias or threshold. DaveWF 07:33, 4 April 2006 (UTC)
[edit] What does b represent?
My lay understanding of scientific, computer, and math topics is good but my understanding of jargon and formulae is poor...
In the definition section we have f(x) = <w,x> + b - all terms are defined except b — what am I missing? — Hippietrail 16:53, 10 April 2006 (UTC)
- I've tried to clear this up. Hope that helpsl. DaveWF 06:22, 5 September 2006 (UTC)
[edit] Learning a linear MLP that does XOR
Multiple layers don't help unless they have non-linear activation functions, since any number of linear layers will still give you a linear decision boundary. I'll fix this up later tonight. DaveWF 22:26, 4 September 2006 (UTC)
- Okay, I've fixed some bias-related stuff but I'm going to do a major rewrite over the next couple of days. The superficial discussion is fine but some of the other stuff is inaccurate or misleading. DaveWF 06:22, 5 September 2006 (UTC)
[edit] Learning rule & bias?
The article suggests that the bias is not adjusted by the perceptron learning rule, but AFAIK this is usually not the case. Can someone who knows more about NNs confirm this? Neilc 23:48, 15 October 2006 (UTC)
The bias can be learned just like any other weight, and often is by means of introduction of another input which is always '1'. In this way it's quite similar to linear regression. DaveWF 04:03, 18 October 2006 (UTC)
[edit] Content difficult to understand
I think the article should be updated a little. even the second paragraph is a little confusing, when it calls the perceptron a 'binary classifier'. I will try to draw some pictures, and I think some of the mathematical text should either be cleaned up, or replaced with picturesPaskari 17:37, 29 November 2006 (UTC)
I updated the 'learning algorithm' section, as I found it to be a little difficult to follow. My version is better in that it is tabular, but I still think it's too dificult too follow. Could someone look it over and update as need be. Paskari 19:28, 29 November 2006 (UTC)
[edit] Multi Layer Perceptron
Could someone who is knowledgable enough create a page for multi-layer perceptrons? there is a good section on it under Artificial Neural Networks, but it isn't detailed enough. Paskari 19:34, 29 November 2006 (UTC)
[edit] Running Time
I am going to create a section outlining the running time and tractability of the algorithm, I hope this is OK with everyone. Paskari 16:53, 1 December 2006 (UTC)
