Objectives 4 Perceptron Learning Rule

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Objectives4-1 4 4 Perceptron Learning Rule Objectives4-1Theory and Examples4-2Learning Rules4-2Perceptron Architecture4-3Single-Neuron Perceptron4-5Multiple-Neuron Perceptron4-8Perceptron Learning Rule4-8Test Problem4-9Constructing Learning Rules4-10Unified Learning Rule4-12Training Multiple-Neuron Perceptrons4-13Proof of Convergence4-15Notation4-15Proof4-16Limi tations4-18Summary of Results4-20Solved Problems4-21Epilogue4-33Further Reading4-34Exercises4-36 Objectives One of the questions we raised in Chapter 3 was: How do we determine the weight matrix and bias for Perceptron networks with many inputs, where it is impossible to visualize the decision boundaries? In this chapter we will describe an algorithm for training Perceptron networks, so that they can learn to solve classification problems. We will begin by explaining what a Learning rule is and will then develop the Perceptron Learning rule. We will conclude by discussing the advantages and limitations of the single-layer Perceptron network.

Perceptron Learning Rule Objectives 4-1 Theory and Examples 4-2 Learning Rules 4-2 Perceptron Architecture 4-3 Single-Neuron Perceptron 4-5 Multiple-Neuron Perceptron 4-8 Perceptron Learning Rule 4-8 Test Problem 4-9 Constructing Learning Rules 4-10 Unified Learning Rule 4-12 Training Multiple-Neuron Perceptrons 4-13 Proof of Convergence 4-15 ...

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