Home Learning from Data: Concepts, Theory, and Methods by Vladimir Cherkassky; Filip M. Mulier (ISBN: 9780471681823)

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Learning from Data: Concepts, Theory, and Methods Hardcover - 2007 - 2nd Edition

Name: Learning from Data: Concepts, Theory, and Methods
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Author: Vladimir Cherkassky
ISBN: 9780471681823

by Vladimir Cherkassky; Filip M. Mulier

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From the rear cover

An interdisciplinary framework for learning methodologies--now revised and updated

Learning from Data provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and pattern recognition can be applied--showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science.

Since the first edition was published, the field of data-driven learning has experienced rapid growth. This Second Edition covers these developments with a completely revised chapter on support vector machines, a new chapter on noninductive inference and alternative learning formulations, and an in-depth discussion of the VC theoretical approach as it relates to other paradigms.

Complete with over one hundred illustrations, case studies, examples, and chapter summaries, Learning from Data accommodates both beginning and advanced graduate students in engineering, computer science, and statistics. It is also indispensable for researchers and practitioners in these areas who must understand the principles and methods for learning dependencies from data.

Details

Title Learning from Data: Concepts, Theory, and Methods
Author Vladimir Cherkassky; Filip M. Mulier
Binding Hardcover
Edition number 2nd
Edition 2
Pages 560
Volumes 1
Language ENG
Publisher Wiley-IEEE Press, New Jersey
Date 2007-08-24
Features Bibliography, Index, Table of Contents
ISBN 9780471681823 / 0471681822
Weight 2.01 lbs (0.91 kg)
Dimensions 9.19 x 6.5 x 1.25 in (23.34 x 16.51 x 3.18 cm)
Library of Congress subjects Fuzzy systems, Neural networks (Computer science)
Library of Congress Catalog Number 2006038736
Dewey Decimal Code 006.31

Media reviews

Citations

Scitech Book News, 12/01/2007, Page 151

About the author

Vladimir CherKassky, PhD, is Professor of Electrical and Computer Engineering at the University of Minnesota. He is internationally known for his research on neural networks and statistical learning.

Filip Mulier, PhD, has worked in the software field for the last twelve years, part of which has been spent researching, developing, and applying advanced statistical and machine learning methods. He currently holds a project management position.

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Learning from Data: Concepts, Theory, and Methods

by Vladimir Cherkassky

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Hardback. New. An interdisciplinary framework for learning methodologies, covering statistics, neural networks, and fuzzy logic, Learning from Data provides a unified treatment of the principles and methods for learning dependencies from data.