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Ch. 20 – Neural Networks. Supplemental slides for CSE 327 Prof. Jeff Heflin. A Neuron. Perceptron Learning. function PERCEPTRON-LEARNING( examples,network) returns a perceptron hypothesis

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Ch 20 neural networks l.jpg

Ch. 20 – Neural Networks

Supplemental slides for CSE 327

Prof. Jeff Heflin



Perceptron learning l.jpg
Perceptron Learning

function PERCEPTRON-LEARNING(examples,network)returns a perceptron hypothesis

inputs:examples, a set of examples with input x and output y network, a perceptron with weights Wj and activation function g

repeat for each einexamples doErr y[e] – g(in)Wj  Wj +   Err  g’(in)  xj[e]until some stopping criteria is satisfiedreturn NEURAL-NET-HYPOTHESIS(network)

From Figure 20.21, p. 742


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