Overview

This monograph provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the computationally intensive methodology that has enabled their highly successful application to complex problems of pattern classification, forecasting, regression, and nonlinear systems modeling. The reader is provided with the information needed to make practical use of the powerful modeling and design tool of feedforward neural networks, as well as presented with the background needed to make contributions to several research frontiers. This work is therefore of interest to those in electrical engineering, operations research, computer science, and statistics who would like to use nonlinear modeling of stochastic phenomena to treat problems of pattern classification, forecasting, signal processing, machine intelligence, and nonlinear regression. T. L. Fine is Professor of Electrical Engineering at Cornell University.

Full Product Details

Author:   Terrence L. Fine
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   1999 ed.
Dimensions:   Width: 15.50cm , Height: 2.00cm , Length: 23.50cm
Weight:   0.730kg
ISBN:  

9780387987453

ISBN 10:   0387987452
Pages:   340
Publication Date:   11 June 1999
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Objectives, Motivation, Background, and Organization.- Perceptions—Networks with a Single Node.- Feedforward Networks I: Generalities and LTU Nodes.- Feedforward Networks II: Real-Valued Nodes.- Algorithms for Designing Feedforward Networks.- Architecture Selection and Penalty Terms.- Generalization and Learning.

Reviews

From the reviews: <p>JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION <p>.,. Fine must be congratulated for a coherent presentation of carefully selected material. Given the diversity of the field, this represented a serious challenge. Again, Feeforward Neural Network Methodlogy is an excellent reference for whoever wants to be brought to the frontier of research. I enthusiastically recommend it. <p>

From the reviews: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION ...Fine must be congratulated for a coherent presentation of carefully selected material. Given the diversity of the field, this represented a serious challenge. Again, Feeforward Neural Network Methodlogy is an excellent reference for whoever wants to be brought to the frontier of research. I enthusiastically recommend it.

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