Innovations in Machine Learning: Theory and Applications / Edition 1

by Dawn E. Holmes

Overview

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.

Product Details

ISBN-13:	9783642067884
Publisher:	Springer Berlin Heidelberg
Publication date:	11/23/2010
Series:	Studies in Fuzziness and Soft Computing , #194
Edition description:	Softcover reprint of hardcover 1st ed. 2006
Pages:	276
Product dimensions:	6.10(w) x 9.25(h) x (d)

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.