Authors:
- Concentrates on one specific architecture and learning rule which no other book does
- State of the art in artificial neural networks which use Hebbian learning
- A comparative study of a variety of techniques that have been drawn from extensions of one network
- The close link between statistics and artificial neural networks is made clear
- No other direct competition
Part of the book series: Advanced Information and Knowledge Processing (AI&KP)
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Table of contents (15 chapters)
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Front Matter
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Introduction
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Single Stream Networks
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Front Matter
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Back Matter
About this book
Reviews
From the reviews of the first edition:
"This book is concerned with developing unsupervised learning procedures and building self organizing network modules that can capture regularities of the environment. … the book provides a detailed introduction to Hebbian learning and negative feedback neural networks and is suitable for self-study or instruction in an introductory course." (Nicolae S. Mera, Zentralblatt MATH, Vol. 1069, 2005)
Authors and Affiliations
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Applied Computational Intelligence Research Unit, The University of Paisley, UK
Colin Fyfe
Bibliographic Information
Book Title: Hebbian Learning and Negative Feedback Networks
Authors: Colin Fyfe
Series Title: Advanced Information and Knowledge Processing
DOI: https://doi.org/10.1007/b138856
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London 2005
Hardcover ISBN: 978-1-85233-883-1Published: 05 January 2005
Softcover ISBN: 978-1-84996-945-1Published: 22 October 2010
eBook ISBN: 978-1-84628-118-1Published: 07 June 2007
Series ISSN: 1610-3947
Series E-ISSN: 2197-8441
Edition Number: 1
Number of Pages: XVIII, 383
Topics: Artificial Intelligence, Probability and Statistics in Computer Science, Pattern Recognition, Simulation and Modeling, Computer Science, general