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Learning from data = concepts, theory, and methods /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Learning from data/ Vladimir Cherkassky, Filip Mulier.
Reminder of title:	concepts, theory, and methods /
Author:	Cherkassky, Vladimir S.
other author:	Mulier, Filip.
Published:	Hoboken, N.J. :IEEE Press : : c2007.,
Description:	1 online resource (xviii, 538 p.) :ill. :
Subject:	Adaptive signal processing. -
Online resource:	http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5201503
ISBN:	9780470140529

Learning from data = concepts, theory, and methods /
Cherkassky, Vladimir S.

Learning from dataconcepts, theory, and methods /[electronic resource] :Vladimir Cherkassky, Filip Mulier. - 2nd ed. - Hoboken, N.J. :IEEE Press :c2007. - 1 online resource (xviii, 538 p.) :ill.

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.

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.

ISBN: 9780470140529

Standard No.: 10.1002/9780470140529doi

Source: 9780470140529IEEEhttp://ieeexplore.ieee.org

UkSubjects--Topical Terms:

557333
Adaptive signal processing.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: TK5102.9 / .C475 2007

Dewey Class. No.: 006.3/1

Learning from data = concepts, theory, and methods /
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505 0 $a 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.
520 $a 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.
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