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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Order before 4pm tomorrow for delivery by Mon 19 Jan 2026.

A Hardback by Terrence L. Fine

15 in stock


    View other formats and editions of Feedforward Neural Network Methodology Information Science and Statistics by Terrence L. Fine

    Publisher: Springer New York
    Publication Date: 6/11/1999 12:00:00 AM
    ISBN13: 9780387987453, 978-0387987453
    ISBN10: 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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