Footnote:
Includes bibliographical references (p. 519-531) and index. - Description based on print version record
Description:
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 -- Noninductive inference and alternative learning formulations
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