Feedforward Neural Network Methodology by Terrence L. Fine (English) Hardcover B

Description: Feedforward Neural Network Methodology by Terrence L. Fine This monograph provides an introduction to the mathematical properties of feedforward neural networks and to the methodology that has enabled their highly successful application to complex problems of pattern classification, forecasting, regression, and nonlinear systems modeling. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. 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. Review 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." Long Description The decade prior to publication has seen an explosive growth in com- tational speed and memory and a rapid enrichment in our understa- ing of articial neural networks. These two factors have cooperated to at last provide systems engineers and statisticians with a working, prac- cal, and successful ability to routinely make accurate complex, nonlinear models of such ill-understood phenomena as physical, economic, social, and information-based time series and signals and of the patterns h- den in high-dimensional data. The models are based closely on the data itself and require only little prior understanding of the stochastic mec- nisms underlying these phenomena. Among these models, the feedforward neural networks, also called multilayer perceptrons, have lent themselves to the design of the widest range of successful forecasters, pattern clas- ?ers, controllers, and sensors. In a number of problems in optical character recognition and medical diagnostics, such systems provide state-of-the-art performance and such performance is also expected in speech recognition applications. The successful application of feedforward neural networks to time series forecasting has been multiply demonstrated and quite visibly so in the formation of market funds in which investment decisions are based largely on neural network-based forecasts of performance. The purpose of this monograph, accomplished by exposing the meth- ology driving these developments, is to enable you to engage in these - plications and, by being brought to several research frontiers, to advance the methodology itself. Review Quote 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." Details ISBN0387987452 Author Terrence L. Fine Short Title FEEDFORWARD NEURAL NETWORK MET Language English ISBN-10 0387987452 ISBN-13 9780387987453 Media Book Format Hardcover Year 1999 Imprint Springer-Verlag New York Inc. Place of Publication New York, NY Country of Publication United States Residence Ithaca, NY, US Pages 340 DOI 10.1007/b73769;10.1007/978-0-387-22649-1 Edited by Nair, V. AU Release Date 1999-06-11 NZ Release Date 1999-06-11 US Release Date 1999-06-11 UK Release Date 1999-06-11 Publisher Springer-Verlag New York Inc. Edition Description 1999 ed. Series Information Science and Statistics Edition 1999th Publication Date 1999-06-11 Alternative 9781475773095 DEWEY 006.3 Illustrations XVI, 340 p. Audience Undergraduate We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:96233629;

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