Description

Book Synopsis
This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks.

Trade 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."



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.

Feedforward Neural Network Methodology Information Science and Statistics

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    A Hardback by Terrence L. Fine

    £44.99

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      Book details

      Publisher Springer New York
      Published 6 November 1999
      ISBN-13 9780387987453
      978-0387987453
      ISBN-10 0387987452
      Also in:
      Cybernetics and systems theory

      Description

      Book Synopsis
      This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks.

      Trade 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."



      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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