Innovations in Machine Learning Theory and Applications

Innovations in Machine Learning Theory and Applications / edited by Dawn E. Holmes.

Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neura...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Holmes, Dawn E. (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
Edition:1st ed. 2006.
Series:Studies in Fuzziness and Soft Computing, 194
Springer eBook Collection.
Subjects:
Applied mathematics.
Engineering mathematics.
Artificial intelligence.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Description
Summary:Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.
Physical Description:XVI, 276 p. online resource.
ISBN:9783540334866
ISSN:1434-9922 ;
DOI:10.1007/3-540-33486-6

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