Learning from data : concepts, theory, and methods / Vladimir Cherkassky, Filip Mulier.

"A Wiley-Interscience publication."

Includes bibliographical references and index.

Problem Statement, Classical Approaches, and Adaptive Learning -- Regularization Framework -- Statistical Learning Theory -- Nonlinear Optimization Strategies -- Methods for Data Reduction and Dimensionality Reduction -- Methods for Regression -- Classification -- Support Vector Machines -- Fuzzy Systems -- Appendices -- Index.

An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic This book 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 fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples, Learning from Data: * Relates statistical formulation with the latest methodologies used in artificial neural networks, fuzzy systems, and wavelets * Features consistent terminology, chapter summaries, and practical research tips * Emphasizes the conceptual framework provided by Statistical Learning Theory (VC-theory) rather than its commonly practiced mathematical aspects * Provides a detailed description of the new learning methodology called Support Vector Machines (SVM) This invaluable text/reference 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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