Learning from Data: Concepts, Theory, and Methods / Edition 2

Learning from Data: Concepts, Theory, and Methods / Edition 2

by Vladimir Cherkassky, Filip M. Mulier
     
 

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

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Overview

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.

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Product Details

ISBN-13:
9780471681823
Publisher:
Wiley
Publication date:
08/17/2007
Edition description:
REV
Pages:
538
Product dimensions:
6.46(w) x 9.33(h) x 1.28(d)

Meet the Author

VLADIMIR CHERKASSKY is on the faculty of electrical and computer engineering at the University of Minnesota. His current research is on neural network and statistical methods for estimating dependencies from data. Professor Cherkassky is on the governing board of the International Neural Network Society (INNS). He was an organizer of the NATO Advanced Study Institute symposium, From Statistics to Neural Networks, held in France in 1993. FILIP MULIER received a PhD in electrical engineering from the University of Minnesota in 1994. He currently works with a large multinational corporation on industrial applications of learning methods. His current research is on practical applications of learning theory, including industrial process control and financial market prediction.

Table of Contents

Preface
Notation
1Introduction1
2Problem Statement, Classical Approaches, and Adaptive Learning15
3Regularization Framework59
4Statistical Learning Theory92
5Nonlinear Optimization Strategies130
6Methods for Data Reduction and Dimensionality Reduction161
7Methods for Regression215
8Classification303
9Support Vector Machines353
10Fuzzy Systems388
App. AReview of Nonlinear Optimization423
App. BEigenvalues and Singular Value Decomposition431
Index437

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