Learning from Data: Concepts, Theory, and Methods

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John Wiley & Sons, Sep 10, 2007 - Computers - 624 pages
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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.
 

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Contents

PREFACE
xi
NOTATION
xvii
1 Introduction
1
2 Problem Statement Classical Approaches and Adaptive Learning
19
3 Regularization Framework
61
4 Statistical Learning Theory
99
5 Nonlinear Optimization Strategies
151
6 Methods for Data Reduction and Dimensionality Reduction
177
8 Classification
340
9 Support Vector Machines
404
10 Noninductive Inference and Alternative Learning Formulations
467
11 Concluding Remarks
499
Appendix A Review of Nonlinear Optimization
507
Appendix B Eigenvalues and Singular Value Decomposition
514
References
519
Index
533

7 Methods for Regression
249

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About the author (2007)

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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