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4 Perceptron Learning - fu-berlin.de

R. Rojas: Neural Networks, Springer-Verlag, Berlin, 19964 Perceptron Learning algorithms for neural networksIn the two preceding chapters we discussed two closely related models,McCulloch Pitts units and perceptrons, but the question ofhow to find theparameters adequate for a given task was left open. If two sets of points haveto be separated linearly with a Perceptron , adequate weights for the comput-ing unit must be found. The operators that we used in the preceding chapter,for example for edge detection, used hand customized weights. Now we wouldlike to find those parameters automatically. Theperceptron Learning algorithmdeals with this Learning algorithm is an adaptive method by which a networkof com-puting units self-organizes to implement the desired behavior. This is done insome Learning algorithms by presenting some examples of thedesired input-output mapping to the network.

“learning”. A learning algorithm must adapt the network parameters accord-ing to previous experience until a solution is found, if it exists. 4.1.1 Classes of learning algorithms Learning algorithms can be divided into supervised and unsupervised meth-ods. Supervised learning denotes a method in which some input vectors are

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