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Neural Networks

An Introductory Guide for Social Scientists

By: George David Garson

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Hardcover | 1 November 1998

At a Glance

Hardcover
208 Pages

Dimensions(cm)
23.39 x 15.6 x 1.27

Hardcover

$401.50

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Neural networks have influenced many areas of research but have only just started to be utilized in social science research. Neural Networks provides the first accessible introduction to this analysis as a powerful method for social scientists. It provides numerous studies and examples that illustrate the advantages of neural network analysis over other quantitative and modeling methods in wide-spread use among social scientists. The author, G. David Garson, presents the methods in an accessible style for the reader who does not have a background in computer science. Features include an introduction to the vocabulary and framework of neural networks, a concise history of neural network methods, a substantial review of the literature, detailed neural network applications in the social sciences, coverage of the most common alternative neural network models, methodological considerations in applying neural networks, examples using the two leading software packages for neural network analysis, and numerous illustrations and diagrams. This introductory guide to using neural networks in the social sciences will enable students, researchers, and professionals to utilize these important new methods in their research and analysis.

Industry Reviews

`Garson's book would be a good buy for someone setting out to apply neural networks to their data. It takes a balanced approach, trying to make it clear where they would be applicable and where traditional statisitcs might be a better bet. It is certainly easy to read' - British Journal of Mathematical and Statisistical Psychology `A useful reference source for terminology, mathematical background, possible application areas and pointers towards software use' - Statistical Methods in Medical Research The much of the material within is timeless and the quality of its presentation allows it to remain a value-add contributor, even today. Overall this book needs to be taken off the storage shelf, dusted off, and placed on your lap. The book's publication age is an advantage in this case as the all-important basics of neural networks are not skimmed over in this book as they often can be the books published today. This is a must-read for any computational modeler looking to a way to progress their technique. -- Terrill L. Frantz

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