Neural Networks

This book provides the first accessible introduction to neural network analysis as a methodological strategy for social scientists. The author details numerous studies and examples which illustrate the advantages of neural network analysis over other quantitative and modeling methods in widespread use. Methods are presented in an accessible style for readers who do not have a background in computer science. The book provides a history of neural network methods, a substantial review of the literature, detailed applications, coverage of the most common alternative models and examples of two leading software packages for neural network analysis.

Series Editor's Introduction

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Notations

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1 Introduction

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1.1 What Are Neural Networks?

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1.2 Overview of This Book

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2 The Perceptron

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2.1 Overview

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2.2 The Building Block: The McCulloch and Pitts Neuron

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2.3 Architecture of a Perceptron

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2.4 The Widrow-Hoff Learning Rule

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2.5 Learning With +1/ - 1 Cells

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2.6 Performance Evaluation

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2.7 The Perceptron and Discriminant Analysis

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2.8 Learning and Testing Sets: The Validation Problem

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3 Linear Autoassociative Memories

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3.1 Overview

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3.2 The Building Block: The Basic Linear Unit

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3.3 Architecture of an Autoassociative Memory

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3.4 The Hebbian Learning Rule

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3.5 Retrieval of a Learned Pattern

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3.6 Limitations of Hebbian Learning

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3.7 Generalization to New Stimuli

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3.8 Reconstruction Error, Error Term, and Global Error

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3.9 The Widrow-Hoff Learning Rule

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3.10 Eigen and Singular Value Decompositions: PCA

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3.11 The Widrow-Hoff Rule and Gradient Descent

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4 Linear Heteroassociative Memories

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4.1 Overview

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4.2 Architecture of a Heteroassociative Memory

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4.3 The Hebbian Learning Rule

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4.4 The Widrow-Hoff Learning Rule

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4.5 Widrow-Hoff, Pseudo-Inverse, and Linear Regression

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4.6 The Widrow-Hoff Learning Rule and Gradient Descent

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4.7 Discriminant Analysis and Perceptron Revisited

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4.8 Radial Basis Function Networks

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5 Error Backpropagation

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5.1 Overview

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5.2 Architecture and Notation

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5.3 The Building Block: The Nonlinear Unit

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5.4 The Backpropagation Algorithm

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5.5 Performance Analysis

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5.6 Backpropagation and Gradient Descent

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5.7 Backpropagation and Logistic Regression

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6 Useful References

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References

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About the Authors

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