Cherkassky, Vladimir S.
Learning from data : concepts, theory, and methods / Vladimir Cherkassky, Filip Mulier. - 2nd ed. - 1 PDF (xviii, 538 pages) : illustrations.
Includes bibliographical references (p. 519-531) 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 -- Noninductive inference and alternative learning formulations.
Restricted to subscribers or individual electronic text purchasers.
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.
Electronic reproduction.
Piscataway, N.J. :
IEEE,
2010.
Mode of access: World Wide Web.
System requirements: Web browser.
Title from title screen (viewed on Oct. 7, 2010).
Access may be restricted to users at subscribing institutions.
Mode of access: World Wide Web.
9780470140529
10.1002/9780470140529 doi
Adaptive signal processing.
Machine learning.
Neural networks (Computer science)
Fuzzy systems.
Electronic books.
TK5102.9 / .C475 2007eb
006.3/1
Learning from data : concepts, theory, and methods / Vladimir Cherkassky, Filip Mulier. - 2nd ed. - 1 PDF (xviii, 538 pages) : illustrations.
Includes bibliographical references (p. 519-531) 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 -- Noninductive inference and alternative learning formulations.
Restricted to subscribers or individual electronic text purchasers.
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.
Electronic reproduction.
Piscataway, N.J. :
IEEE,
2010.
Mode of access: World Wide Web.
System requirements: Web browser.
Title from title screen (viewed on Oct. 7, 2010).
Access may be restricted to users at subscribing institutions.
Mode of access: World Wide Web.
9780470140529
10.1002/9780470140529 doi
Adaptive signal processing.
Machine learning.
Neural networks (Computer science)
Fuzzy systems.
Electronic books.
TK5102.9 / .C475 2007eb
006.3/1