Learning from data : concepts, theory, and methods
Title:
Learning from data : concepts, theory, and methods
Author:
Cherkassky, Vladimir S. author.
ISBN:
9780470140529
Personal Author:
Edition:
2nd ed.
Physical Description:
1 PDF (xviii, 538 pages) : illustrations.
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 -- Noninductive inference and alternative learning formulations.
Abstract:
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.
Additional Physical Form Available Note:
Also available in print.
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Electronic Access:
Abstract with links to resource http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5201503