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Learning from data = concepts, theor...
~
Mulier, Filip.
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:
xviii, 538 p. :ill. ; : 25 cm.;
Subject:
Adaptive signal processing. -
Online resource:
An electronic book accessible through the World Wide Web; click for information
ISBN:
9780470140529
Learning from data = concepts, theory, and methods /
Cherkassky, Vladimir S.
Learning from data
concepts, theory, and methods /[electronic resource] :Vladimir Cherkassky, Filip Mulier. - 2nd ed. - Hoboken, N.J. :IEEE Press :c2007. - xviii, 538 p. :ill. ;25 cm.
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.
Electronic reproduction.
Piscataway, N.J. :
IEEE,
2010.
Mode of access: World Wide Web.
ISBN: 9780470140529
Source: 9780470140529IEEEhttp://ieeexplore.ieee.orgSubjects--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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OCoLC
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[electronic resource] :
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concepts, theory, and methods /
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Vladimir Cherkassky, Filip Mulier.
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c2007.
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xviii, 538 p. :
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ill. ;
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25 cm.
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Includes bibliographical references (p. 519-531) and index.
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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.
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Electronic reproduction.
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Piscataway, N.J. :
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IEEE,
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2010.
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An electronic book accessible through the World Wide Web; click for information
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TWNFU
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