Overview
- There are no other books covering both modular and ensemble approaches (The ensemble approach uses a variety of methods to create a set of different nets trained on the same task; the modular approach decomposes a task into simpler problems)
- The presentation of techniques is accompanied by analysis and evaluation of their relative effectiveness on a variety of problems The book focuses on the combination of neural nets, but many of the methods are applicable to a wider variety of statistical methods
Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Editors and Affiliations
Bibliographic Information
Book Title: Combining Artificial Neural Nets
Book Subtitle: Ensemble and Modular Multi-Net Systems
Editors: Amanda J. C. Sharkey
Series Title: Perspectives in Neural Computing
DOI: https://doi.org/10.1007/978-1-4471-0793-4
Publisher: Springer London
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag London Limited 1999
Softcover ISBN: 978-1-85233-004-0Published: 22 January 1999
eBook ISBN: 978-1-4471-0793-4Published: 06 December 2012
Series ISSN: 1431-6854
Edition Number: 1
Number of Pages: XV, 298
Number of Illustrations: 6 b/w illustrations
Topics: Artificial Intelligence, Computational Biology/Bioinformatics, Complex Systems, Statistical Physics and Dynamical Systems