Feedforward Neural Network Methodology
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Feedforward Neural Network Methodology

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Description

This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.
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Product details

  • Paperback | 340 pages
  • 155 x 235 x 19.05mm | 551g
  • New York, NY, United States
  • English
  • Softcover reprint of the original 1st ed. 1999
  • XVI, 340 p.
  • 1475773099
  • 9781475773095

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.
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Review Text

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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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."
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Rating details

3 ratings
4 out of 5 stars
5 67% (2)
4 0% (0)
3 0% (0)
2 33% (1)
1 0% (0)
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