Terrence L. Fine

Feedforward Neural Network Methodology (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 340
• Erscheinungstermin: 6. April 2006
• Englisch
• ISBN-13: 9780387226491
• Artikelnr.: 43999164

Inhaltsangabe

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.

Rezensionen

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."