南開科技大學圖書館 |

Language: English

Help

南開科技大學

圖書館首頁

編目中圖書申請

Back

Switch To: Labeled | MARC Mode | ISBD

Advances in kernel methodssupport vector learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in kernel methods/ edited by Bernhard Sch鋌lkopf, Christopher J.C. Burges, Alexander J. Smola.
Reminder of title:	support vector learning /
other author:	Sch鋌lkopf, Bernhard.
Published:	Cambridge, Mass. :MIT Press,�999.
Description:	1 online resource (vii, 376 pages) :illustrations
Subject:	Machine learning. -
Online resource:	Click here for online access to this book (查閱全文) (EBSCO eBook)
ISBN:	0585128294
ISBN:	9780585128290
ISBN:	9780262194167 (alk. paper)
ISBN:	0262194163 (alk. paper)

Advances in kernel methodssupport vector learning /
Advances in kernel methodssupport vector learning /[electronic resource] :edited by Bernhard Sch鋌lkopf, Christopher J.C. Burges, Alexander J. Smola. - Cambridge, Mass. :MIT Press,�999. - 1 online resource (vii, 376 pages) :illustrations

Includes bibliographical references (pages 353-371) and index.

Preface -- Introduction to support vector learning -- Roadmap -- Three remarks on the support vector method of function estimation / Valdimir Vapnik -- Generalization performance of support vector machines and other pattern classifiers / Peter Bartlett and John Shawe-Taylor -- Bayesian voting schemes and large margin classifiers / Nello Cristianini and John Shawe-Taylor -- Support vector machines, reproducing kernal Hilbert spaces, and randomized GACV / Grace Wahba -- Geometry and invariance in kernel based methods / Christopher J.C. Burgess -- On the annealed VC entropy for margin classifiers : a statistical mechanics study / Manfred Opper -- Entropy numbers, operators and support vector kernels / Robert C. Williamson, Alex J. Smola and Berhard Sch鋌lkopf -- Solving the quadratic programming problem arising in support vector classification / Linda Kaufman -- Making large-scale support vector machine learning practical / Thorsten Joachims -- Fast training of support vector machines using sequential minimal optimization / John C. Platt -- Support vector machines for dynamic reconstruction of a chaotic system / David Mattera and Simon Haykin -- Using support vector machines for time series prediction / Klaus-Robert M鋎ller . [and others] -- Pairwise classification and support vector machines / Ulrich Kressel -- Reducing the run-time complexity in support vector machines / Edgar E. Osuna and Federico Girosi -- Support vector regression with ANOVA decomposition kernels / Mark O. Stitson . [and others] -- Support vector density estimation / Jason Weston . [et al.] -- Combining support vector and mathematical programming methods for classification / Kristin P. Bennett -- Kernel principal component analysis / Berhard Sch鋌lkopf, Alex J. Smola and Klaus-Robert M鋎ller.

ISBN: 0585128294

LCCN: 98040302 Subjects--Topical Terms:

147118
Machine learning.
Index Terms--Genre/Form:

172687
Electronic books.

LC Class. No.: Q325.5 / .A32 1999eb

Dewey Class. No.: 006.3/1

Advances in kernel methodssupport vector learning /
LDR:03662nmm 2200409La 4500 001 1000051003
005 20150319095019.0
006 m o u
007 cr cn|||||||||
008 150529s1999 maua ob 001 0 eng d
010 $a 98040302
019 $a 71799403 $a 532592519 $a 610526709
020 $a 0585128294 $q (electronic bk.)
020 $a 9780585128290 $q (electronic bk.)
020 $z 0262194163 $q (alk. paper)
020 $a 9780262194167 (alk. paper)
020 $a 0262194163 (alk. paper)
035 $a (OCoLC)44957981 $z (OCoLC)71799403 $z (OCoLC)532592519 $z (OCoLC)610526709
035 $a ocm44957981
040 $a N$T $b eng $e pn $c N$T $d OCL $d OCLCQ $d ZID $d OCLCQ $d YDXCP $d OCLCQ $d TUU $d OCLCQ $d TNF $d OCLCQ $d ZCU $d OCLCO $d OCLCF $d OCLCQ $d COO $d CUS $d MYG $d NLGGC $d OCLCQ $d IL4I4
049 $a MAIN{me_controlnum}
050 4 $a Q325.5 $b .A32 1999eb
072 7 $a COM $x 005030 $2 bisacsh
072 7 $a COM $x 004000 $2 bisacsh
082 0 4 $a 006.3/1 $2 21
245 0 0 $a Advances in kernel methods $h [electronic resource] : $b support vector learning / $c edited by Bernhard Sch鋌lkopf, Christopher J.C. Burges, Alexander J. Smola.
260 $a Cambridge, Mass. : $b MIT Press, $c �999.
300 $a 1 online resource (vii, 376 pages) : $b illustrations
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
504 $a Includes bibliographical references (pages 353-371) and index.
505 0 $a Preface -- Introduction to support vector learning -- Roadmap -- Three remarks on the support vector method of function estimation / Valdimir Vapnik -- Generalization performance of support vector machines and other pattern classifiers / Peter Bartlett and John Shawe-Taylor -- Bayesian voting schemes and large margin classifiers / Nello Cristianini and John Shawe-Taylor -- Support vector machines, reproducing kernal Hilbert spaces, and randomized GACV / Grace Wahba -- Geometry and invariance in kernel based methods / Christopher J.C. Burgess -- On the annealed VC entropy for margin classifiers : a statistical mechanics study / Manfred Opper -- Entropy numbers, operators and support vector kernels / Robert C. Williamson, Alex J. Smola and Berhard Sch鋌lkopf -- Solving the quadratic programming problem arising in support vector classification / Linda Kaufman -- Making large-scale support vector machine learning practical / Thorsten Joachims -- Fast training of support vector machines using sequential minimal optimization / John C. Platt -- Support vector machines for dynamic reconstruction of a chaotic system / David Mattera and Simon Haykin -- Using support vector machines for time series prediction / Klaus-Robert M鋎ller . [and others] -- Pairwise classification and support vector machines / Ulrich Kressel -- Reducing the run-time complexity in support vector machines / Edgar E. Osuna and Federico Girosi -- Support vector regression with ANOVA decomposition kernels / Mark O. Stitson . [and others] -- Support vector density estimation / Jason Weston . [et al.] -- Combining support vector and mathematical programming methods for classification / Kristin P. Bennett -- Kernel principal component analysis / Berhard Sch鋌lkopf, Alex J. Smola and Klaus-Robert M鋎ller.
588 0 $a Print version record.
650 0 $a Machine learning. $3 147118
650 0 $a Algorithms. $3 149403
650 0 $a Kernel functions. $3 166469
650 7 $a COMPUTERS $x Enterprise Applications $x Business Intelligence Tools. $2 bisacsh $3 1000063654
650 7 $a COMPUTERS $x Intelligence (AI) & Semantics. $2 bisacsh $3 1000063655
650 1 7 $a Kunstmatige intelligentie. $2 gtt $3 174275
650 1 7 $a Algoritmen. $2 gtt. $3 1000011984
650 1 7 $a Patroonherkenning. $2 gtt $3 1000078284
650 1 7 $a Functies (wiskunde) $2 gtt $3 1000078285
650 1 7 $a Machine-learning. $2 gtt $3 1000078286
655 4 $a Electronic books. $2 local. $3 172687
655 7 $a Congressen (vorm) $2 gtt $3 1000078122
700 1 $a Sch鋌lkopf, Bernhard. $3 1000072059
700 1 $a Burges, Christopher J. C. $3 1000076306
700 1 $a Smola, Alexander J. $3 181781
776 0 8 $i Print version: $t Advances in kernel methods. $d Cambridge, Mass. : MIT Press, �999 $z 0262194163 $w (DLC) 98040302 $w (OCoLC)39706952
856 4 0 $3 EBSCOhost $u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=421 $z Click here for online access to this book (查閱全文) (EBSCO eBook)
938 $a EBSCOhost $b EBSC $n 421
938 $a YBP Library Services $b YANK $n 2307963