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

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

by Vladimir Cherkassky, Filip M. Mulier
     
 

ISBN-10: 0471681822

ISBN-13: 9780471681823

Pub. Date: 08/17/2007

Publisher: Wiley

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

Overview

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 making this an invaluable text.

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)

Table of Contents

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.

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