Innovations in machine learning : theory and applications /

Innovations in machine learning : theory and applications /

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Bibliographic Details
Imprint:	Berlin ; New York : Springer, ©2006.
Description:	1 online resource (xvi, 274 pages) : illustrations.
Language:	English
Series:	Studies in fuzziness and soft computing, 1860-0808 ; v. 194 Studies in fuzziness and soft computing ; v. 194.
Subject:	Machine learning. Artificial intelligence. Artificial intelligence. Machine learning.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11068937

Hidden Bibliographic Details
Other authors / contributors:	Holmes, Dawn E. Jain, L. C.
ISBN:	9783540334866 3540334866 3540306099 9783540306092 1280610581 9781280610585
Notes:	Includes bibliographical references and index. Print version record.
Summary:	"Machine learning is currently one of the most rapidly growing areas of research in computer science. 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."--Jacket.
Other form:	Print version: Innovations in machine learning. Berlin ; New York : Springer, ©2006 3540306099 9783540306092

Table of Contents:

A Bayesian Approach to Causal Discovery
A Tutorial on Learning Causal Influence
Learning Based Programming
N-1 Experiments Suffice to Determine the Causal Relations Among N Variables
Support Vector Inductive Logic Programming
Neural Probabilistic Language Models
Computational Grammatical Inference
On Kernel Target Alignment
The Structure of Version Space.