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Learning from data = concepts, theor...

Mulier, Filip.

Learning from data = concepts, theory, and methods /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Learning from data/ Vladimir Cherkassky, Filip Mulier.
其他題名:	concepts, theory, and methods /
作者:	Cherkassky, Vladimir S.
其他作者:	Mulier, Filip.
出版者:	Hoboken, N.J. :IEEE Press : : c2007.,
面頁冊數:	xviii, 538 p. :ill. ; : 25 cm.;
標題:	Adaptive signal processing. -
電子資源:	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 dataconcepts, 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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020 $a 0470140526
035 $a (OCoLC)668993792
035 $a ocn668993792
037 $a 9780470140529 $b IEEE $n http://ieeexplore.ieee.org
040 $a IEEEE $b eng $c IEEEE $d OCLCQ
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245 1 0 $a Learning from data $h [electronic resource] : $b concepts, theory, and methods / $c Vladimir Cherkassky, Filip Mulier.
250 $a 2nd ed.
260 $a Hoboken, N.J. : $c c2007. $b IEEE Press : $b Wiley-Interscience,
300 $a xviii, 538 p. : $b ill. ; $c 25 cm.
504 $a Includes bibliographical references (p. 519-531) and index.
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.
533 $a Electronic reproduction. $b Piscataway, N.J. : $c IEEE, $d 2010. $n Mode of access: World Wide Web. $n System requirements: Web browser. $n Title from title screen (viewed on Oct. 7, 2010). $n Access may be restricted to users at subscribing institutions.
650 0 $a Adaptive signal processing. $3 557333
650 0 $a Machine learning. $3 561253
650 0 $a Neural networks (Computer science) $3 528588
650 0 $a Fuzzy systems. $3 528426
650 1 7 $a Statistisch onderzoek. $2 gtt $3 772547
650 1 7 $a Statistische methoden. $2 gtt $3 655206
650 1 7 $a Computermodellen. $2 gtt $3 772548
650 0 7 $a Datenauswertung. $2 swd $3 772549
650 0 7 $a Maschinelles Lernen. $2 swd $3 772550
650 0 7 $a Methode. $2 swd $3 772551
655 4 $a Electronic books. $2 local $3 554714
700 1 $a Mulier, Filip. $3 772546
856 4 0 $3 IEEE Xplore $u http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5201503 $z An electronic book accessible through the World Wide Web; click for information
994 $a 92 $b TWNFU

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