Learning kernel classifiers :: theory and algorithms /
Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©2002.
|
| Schriftenreihe: | Adaptive computation and machine learning.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library. |
| Beschreibung: | 1 online resource (xx, 364 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 339-355) and index. |
| ISBN: | 9780262256339 0262256339 0585436681 9780585436685 9780262083065 026208306X |
Internformat
MARC
| LEADER | 00000cam a2200000 a 4500 | ||
|---|---|---|---|
| 001 | ZDB-4-EBU-ocm51991806 | ||
| 003 | OCoLC | ||
| 005 | 20241004212047.0 | ||
| 006 | m o d | ||
| 007 | cr cn||||||||| | ||
| 008 | 030408s2002 maua ob 001 0 eng d | ||
| 010 | |z 2001044445 | ||
| 040 | |a N$T |b eng |e pn |c N$T |d YDXCP |d OCLCQ |d N$T |d IDEBK |d OCLCQ |d TUU |d OCLCQ |d TNF |d OCLCQ |d IEEEE |d ZCU |d OCLCF |d OCLCQ |d NLGGC |d OCLCQ |d AGLDB |d PIFBR |d ESU |d OCLCQ |d NJR |d WY@ |d ROC |d OCLCQ |d MNS |d LUE |d RCC |d VTS |d MERER |d OCLCQ |d INT |d TOF |d OCLCQ |d MITPR |d STF |d OL$ |d VT2 |d OCLCO |d OCLCQ |d COA |d OCLCO |d OCLCL |d INARC |d SXB | ||
| 019 | |a 961683820 |a 962676938 |a 991953039 |a 1011939695 |a 1020510321 |a 1022038083 |a 1053051972 |a 1286907604 |a 1340067701 |a 1430398089 | ||
| 020 | |a 9780262256339 |q (electronic bk.) | ||
| 020 | |a 0262256339 |q (electronic bk.) | ||
| 020 | |a 0585436681 |q (electronic bk.) | ||
| 020 | |a 9780585436685 |q (electronic bk.) | ||
| 020 | |a 9780262083065 | ||
| 020 | |a 026208306X | ||
| 024 | 8 | |a (WaSeSS)ssj0000190583 | |
| 035 | |a (OCoLC)51991806 |z (OCoLC)961683820 |z (OCoLC)962676938 |z (OCoLC)991953039 |z (OCoLC)1011939695 |z (OCoLC)1020510321 |z (OCoLC)1022038083 |z (OCoLC)1053051972 |z (OCoLC)1286907604 |z (OCoLC)1340067701 |z (OCoLC)1430398089 | ||
| 037 | |a 4170 |b MIT Press | ||
| 037 | |a 9780262256339 |b MIT Press | ||
| 050 | 4 | |a Q325.5 |b .H48 2002eb | |
| 072 | 7 | |a COM |x 005030 |2 bisacsh | |
| 072 | 7 | |a COM |x 004000 |2 bisacsh | |
| 082 | 7 | |a 006.3/1 |2 21 | |
| 049 | |a MAIN | ||
| 100 | 1 | |a Herbrich, Ralf. | |
| 245 | 1 | 0 | |a Learning kernel classifiers : |b theory and algorithms / |c Ralf Herbrich. |
| 260 | |a Cambridge, Mass. : |b MIT Press, |c ©2002. | ||
| 300 | |a 1 online resource (xx, 364 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 | ||
| 347 | |a text file |2 rdaft | ||
| 490 | 1 | |a Adaptive computation and machine learning | |
| 504 | |a Includes bibliographical references (pages 339-355) and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library. | ||
| 505 | 0 | 0 | |g 1. |t Introduction -- |g I. |t Learning Algorithms. |g 2. |t Kernel Classifiers from a Machine Learning Perspective. |g 3. |t Kernel Classifiers from a Bayesian Perspective -- |g II. |t Learning Theory. |g 4. |t Mathematical Models of Learning. |g 5. |t Bounds for Specific Algorithms -- |g III. |t Appendices -- |g A. |t Theoretical Background and Basic Inequalities -- |g B. |t Proofs and Derivations -- Part I -- |g C. |t Proofs and Derivations -- Part II -- |g D. |t Pseudocodes. |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Algorithms. |0 http://id.loc.gov/authorities/subjects/sh85003487 | |
| 650 | 2 | |a Algorithms |0 https://id.nlm.nih.gov/mesh/D000465 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Algorithms |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 653 | |a COMPUTER SCIENCE/Machine Learning & Neural Networks | ||
| 655 | 4 | |a Electronic book. | |
| 758 | |i has work: |a Learning Kernel Classifiers (Text) |1 https://id.oclc.org/worldcat/entity/E39PCXH7q9Mwgk8w7fcw7HBcCP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Herbrich, Ralf. |t Learning kernel classifiers. |d Cambridge, Mass. : MIT Press, ©2002 |z 026208306X |w (DLC) 2001044445 |w (OCoLC)47705793 |
| 830 | 0 | |a Adaptive computation and machine learning. | |
| 856 | 4 | 0 | |l FWS01 |p ZDB-4-EBU |q FWS_PDA_EBU |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=74987 |3 Volltext |
| 938 | |a EBSCOhost |b EBSC |n 74987 | ||
| 938 | |a YBP Library Services |b YANK |n 2332019 | ||
| 938 | |a Internet Archive |b INAR |n learningkernelcl0000herb | ||
| 994 | |a 92 |b GEBAY | ||
| 912 | |a ZDB-4-EBU | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBU-ocm51991806 |
|---|---|
| _version_ | 1816796894532206592 |
| adam_text | |
| any_adam_object | |
| author | Herbrich, Ralf |
| author_facet | Herbrich, Ralf |
| author_role | |
| author_sort | Herbrich, Ralf |
| author_variant | r h rh |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | Q325 |
| callnumber-raw | Q325.5 .H48 2002eb |
| callnumber-search | Q325.5 .H48 2002eb |
| callnumber-sort | Q 3325.5 H48 42002EB |
| callnumber-subject | Q - General Science |
| collection | ZDB-4-EBU |
| contents | Introduction -- Learning Algorithms. Kernel Classifiers from a Machine Learning Perspective. Kernel Classifiers from a Bayesian Perspective -- Learning Theory. Mathematical Models of Learning. Bounds for Specific Algorithms -- Appendices -- Theoretical Background and Basic Inequalities -- Proofs and Derivations -- Part I -- Proofs and Derivations -- Part II -- Pseudocodes. |
| ctrlnum | (OCoLC)51991806 |
| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>05009cam a2200721 a 4500</leader><controlfield tag="001">ZDB-4-EBU-ocm51991806 </controlfield><controlfield tag="003">OCoLC</controlfield><controlfield tag="005">20241004212047.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr cn|||||||||</controlfield><controlfield tag="008">030408s2002 maua ob 001 0 eng d</controlfield><datafield tag="010" ind1=" " ind2=" "><subfield code="z"> 2001044445</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">N$T</subfield><subfield code="b">eng</subfield><subfield code="e">pn</subfield><subfield code="c">N$T</subfield><subfield code="d">YDXCP</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">N$T</subfield><subfield code="d">IDEBK</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">TUU</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">TNF</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">IEEEE</subfield><subfield code="d">ZCU</subfield><subfield code="d">OCLCF</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">NLGGC</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">AGLDB</subfield><subfield code="d">PIFBR</subfield><subfield code="d">ESU</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">NJR</subfield><subfield code="d">WY@</subfield><subfield code="d">ROC</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">MNS</subfield><subfield code="d">LUE</subfield><subfield code="d">RCC</subfield><subfield code="d">VTS</subfield><subfield code="d">MERER</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">INT</subfield><subfield code="d">TOF</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">MITPR</subfield><subfield code="d">STF</subfield><subfield code="d">OL$</subfield><subfield code="d">VT2</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">COA</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCL</subfield><subfield code="d">INARC</subfield><subfield code="d">SXB</subfield></datafield><datafield tag="019" ind1=" " ind2=" "><subfield code="a">961683820</subfield><subfield code="a">962676938</subfield><subfield code="a">991953039</subfield><subfield code="a">1011939695</subfield><subfield code="a">1020510321</subfield><subfield code="a">1022038083</subfield><subfield code="a">1053051972</subfield><subfield code="a">1286907604</subfield><subfield code="a">1340067701</subfield><subfield code="a">1430398089</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780262256339</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0262256339</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0585436681</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780585436685</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780262083065</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">026208306X</subfield></datafield><datafield tag="024" ind1="8" ind2=" "><subfield code="a">(WaSeSS)ssj0000190583</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)51991806</subfield><subfield code="z">(OCoLC)961683820</subfield><subfield code="z">(OCoLC)962676938</subfield><subfield code="z">(OCoLC)991953039</subfield><subfield code="z">(OCoLC)1011939695</subfield><subfield code="z">(OCoLC)1020510321</subfield><subfield code="z">(OCoLC)1022038083</subfield><subfield code="z">(OCoLC)1053051972</subfield><subfield code="z">(OCoLC)1286907604</subfield><subfield code="z">(OCoLC)1340067701</subfield><subfield code="z">(OCoLC)1430398089</subfield></datafield><datafield tag="037" ind1=" " ind2=" "><subfield code="a">4170</subfield><subfield code="b">MIT Press</subfield></datafield><datafield tag="037" ind1=" " ind2=" "><subfield code="a">9780262256339</subfield><subfield code="b">MIT Press</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">Q325.5</subfield><subfield code="b">.H48 2002eb</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="x">005030</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="x">004000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="7" ind2=" "><subfield code="a">006.3/1</subfield><subfield code="2">21</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">MAIN</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Herbrich, Ralf.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Learning kernel classifiers :</subfield><subfield code="b">theory and algorithms /</subfield><subfield code="c">Ralf Herbrich.</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Cambridge, Mass. :</subfield><subfield code="b">MIT Press,</subfield><subfield code="c">©2002.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xx, 364 pages) :</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">text file</subfield><subfield code="2">rdaft</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Adaptive computation and machine learning</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references (pages 339-355) and index.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.</subfield></datafield><datafield tag="505" ind1="0" ind2="0"><subfield code="g">1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">I.</subfield><subfield code="t">Learning Algorithms.</subfield><subfield code="g">2.</subfield><subfield code="t">Kernel Classifiers from a Machine Learning Perspective.</subfield><subfield code="g">3.</subfield><subfield code="t">Kernel Classifiers from a Bayesian Perspective --</subfield><subfield code="g">II.</subfield><subfield code="t">Learning Theory.</subfield><subfield code="g">4.</subfield><subfield code="t">Mathematical Models of Learning.</subfield><subfield code="g">5.</subfield><subfield code="t">Bounds for Specific Algorithms --</subfield><subfield code="g">III.</subfield><subfield code="t">Appendices --</subfield><subfield code="g">A.</subfield><subfield code="t">Theoretical Background and Basic Inequalities --</subfield><subfield code="g">B.</subfield><subfield code="t">Proofs and Derivations -- Part I --</subfield><subfield code="g">C.</subfield><subfield code="t">Proofs and Derivations -- Part II --</subfield><subfield code="g">D.</subfield><subfield code="t">Pseudocodes.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85079324</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Algorithms.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85003487</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Algorithms</subfield><subfield code="0">https://id.nlm.nih.gov/mesh/D000465</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Machine Learning</subfield><subfield code="0">https://id.nlm.nih.gov/mesh/D000069550</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Apprentissage automatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Algorithmes.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">algorithms.</subfield><subfield code="2">aat</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS</subfield><subfield code="x">Enterprise Applications</subfield><subfield code="x">Business Intelligence Tools.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS</subfield><subfield code="x">Intelligence (AI) & Semantics.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Algorithms</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">fast</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">COMPUTER SCIENCE/Machine Learning & Neural Networks</subfield></datafield><datafield tag="655" ind1=" " ind2="4"><subfield code="a">Electronic book.</subfield></datafield><datafield tag="758" ind1=" " ind2=" "><subfield code="i">has work:</subfield><subfield code="a">Learning Kernel Classifiers (Text)</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCXH7q9Mwgk8w7fcw7HBcCP</subfield><subfield code="4">https://id.oclc.org/worldcat/ontology/hasWork</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Herbrich, Ralf.</subfield><subfield code="t">Learning kernel classifiers.</subfield><subfield code="d">Cambridge, Mass. : MIT Press, ©2002</subfield><subfield code="z">026208306X</subfield><subfield code="w">(DLC) 2001044445</subfield><subfield code="w">(OCoLC)47705793</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Adaptive computation and machine learning.</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-4-EBU</subfield><subfield code="q">FWS_PDA_EBU</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=74987</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">74987</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">2332019</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Internet Archive</subfield><subfield code="b">INAR</subfield><subfield code="n">learningkernelcl0000herb</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBU</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| genre | Electronic book. |
| genre_facet | Electronic book. |
| id | ZDB-4-EBU-ocm51991806 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:48:56Z |
| institution | BVB |
| isbn | 9780262256339 0262256339 0585436681 9780585436685 9780262083065 026208306X |
| language | English |
| oclc_num | 51991806 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xx, 364 pages) : illustrations |
| psigel | ZDB-4-EBU |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | MIT Press, |
| record_format | marc |
| series | Adaptive computation and machine learning. |
| series2 | Adaptive computation and machine learning |
| spelling | Herbrich, Ralf. Learning kernel classifiers : theory and algorithms / Ralf Herbrich. Cambridge, Mass. : MIT Press, ©2002. 1 online resource (xx, 364 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rdaft Adaptive computation and machine learning Includes bibliographical references (pages 339-355) and index. Print version record. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library. 1. Introduction -- I. Learning Algorithms. 2. Kernel Classifiers from a Machine Learning Perspective. 3. Kernel Classifiers from a Bayesian Perspective -- II. Learning Theory. 4. Mathematical Models of Learning. 5. Bounds for Specific Algorithms -- III. Appendices -- A. Theoretical Background and Basic Inequalities -- B. Proofs and Derivations -- Part I -- C. Proofs and Derivations -- Part II -- D. Pseudocodes. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Machine learning fast COMPUTER SCIENCE/Machine Learning & Neural Networks Electronic book. has work: Learning Kernel Classifiers (Text) https://id.oclc.org/worldcat/entity/E39PCXH7q9Mwgk8w7fcw7HBcCP https://id.oclc.org/worldcat/ontology/hasWork Print version: Herbrich, Ralf. Learning kernel classifiers. Cambridge, Mass. : MIT Press, ©2002 026208306X (DLC) 2001044445 (OCoLC)47705793 Adaptive computation and machine learning. FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=74987 Volltext |
| spellingShingle | Herbrich, Ralf Learning kernel classifiers : theory and algorithms / Adaptive computation and machine learning. Introduction -- Learning Algorithms. Kernel Classifiers from a Machine Learning Perspective. Kernel Classifiers from a Bayesian Perspective -- Learning Theory. Mathematical Models of Learning. Bounds for Specific Algorithms -- Appendices -- Theoretical Background and Basic Inequalities -- Proofs and Derivations -- Part I -- Proofs and Derivations -- Part II -- Pseudocodes. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85003487 https://id.nlm.nih.gov/mesh/D000465 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Learning kernel classifiers : theory and algorithms / |
| title_alt | Introduction -- Learning Algorithms. Kernel Classifiers from a Machine Learning Perspective. Kernel Classifiers from a Bayesian Perspective -- Learning Theory. Mathematical Models of Learning. Bounds for Specific Algorithms -- Appendices -- Theoretical Background and Basic Inequalities -- Proofs and Derivations -- Part I -- Proofs and Derivations -- Part II -- Pseudocodes. |
| title_auth | Learning kernel classifiers : theory and algorithms / |
| title_exact_search | Learning kernel classifiers : theory and algorithms / |
| title_full | Learning kernel classifiers : theory and algorithms / Ralf Herbrich. |
| title_fullStr | Learning kernel classifiers : theory and algorithms / Ralf Herbrich. |
| title_full_unstemmed | Learning kernel classifiers : theory and algorithms / Ralf Herbrich. |
| title_short | Learning kernel classifiers : |
| title_sort | learning kernel classifiers theory and algorithms |
| title_sub | theory and algorithms / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Machine learning fast |
| topic_facet | Machine learning. Algorithms. Algorithms Machine Learning Apprentissage automatique. Algorithmes. algorithms. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Machine learning Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=74987 |
| work_keys_str_mv | AT herbrichralf learningkernelclassifierstheoryandalgorithms |