Tree-Structured Support Vector Machines for Multi-class Pattern Recognition
Identifieur interne : 002B33 ( Istex/Curation ); précédent : 002B32; suivant : 002B34Tree-Structured Support Vector Machines for Multi-class Pattern Recognition
Auteurs : Friedhelm Schwenker [Suisse, Allemagne] ; Günther Palm [Suisse, Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2001.
Abstract
Abstract: Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVM approach was originally developed for binary classification problems. In this paper SVM architectures for multi-class classification problems are discussed, in particular we consider binary trees of SVMs to solve the multi-class pattern recognition problem. Numerical results for different classifiers on a benchmark data set handwritten digits are presented.
Url:
DOI: 10.1007/3-540-48219-9_41
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: Pour aller vers cette notice dans l'étape Curation :002D59
Links to Exploration step
ISTEX:3719D6AA88CB6ED7B9605980933559F508A74577Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Tree-Structured Support Vector Machines for Multi-class Pattern Recognition</title><author><name sortKey="Schwenker, Friedhelm" sort="Schwenker, Friedhelm" uniqKey="Schwenker F" first="Friedhelm" last="Schwenker">Friedhelm Schwenker</name><affiliation wicri:level="1"><mods:affiliation>Department of Neural Information Processing, University of Ulm, D-89069, Ulm, Switzerland</mods:affiliation><country xml:lang="fr">Suisse</country><wicri:regionArea>Department of Neural Information Processing, University of Ulm, D-89069, Ulm</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: schwenker@informatik.uni-ulm.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author><author><name sortKey="Palm, Gunther" sort="Palm, Gunther" uniqKey="Palm G" first="Günther" last="Palm">Günther Palm</name><affiliation wicri:level="1"><mods:affiliation>Department of Neural Information Processing, University of Ulm, D-89069, Ulm, Switzerland</mods:affiliation><country xml:lang="fr">Suisse</country><wicri:regionArea>Department of Neural Information Processing, University of Ulm, D-89069, Ulm</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: palm@informatik.uni-ulm.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:3719D6AA88CB6ED7B9605980933559F508A74577</idno><date when="2001" year="2001">2001</date><idno type="doi">10.1007/3-540-48219-9_41</idno><idno type="url">https://api.istex.fr/document/3719D6AA88CB6ED7B9605980933559F508A74577/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">002D59</idno><idno type="wicri:Area/Istex/Curation">002B33</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Tree-Structured Support Vector Machines for Multi-class Pattern Recognition</title><author><name sortKey="Schwenker, Friedhelm" sort="Schwenker, Friedhelm" uniqKey="Schwenker F" first="Friedhelm" last="Schwenker">Friedhelm Schwenker</name><affiliation wicri:level="1"><mods:affiliation>Department of Neural Information Processing, University of Ulm, D-89069, Ulm, Switzerland</mods:affiliation><country xml:lang="fr">Suisse</country><wicri:regionArea>Department of Neural Information Processing, University of Ulm, D-89069, Ulm</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: schwenker@informatik.uni-ulm.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author><author><name sortKey="Palm, Gunther" sort="Palm, Gunther" uniqKey="Palm G" first="Günther" last="Palm">Günther Palm</name><affiliation wicri:level="1"><mods:affiliation>Department of Neural Information Processing, University of Ulm, D-89069, Ulm, Switzerland</mods:affiliation><country xml:lang="fr">Suisse</country><wicri:regionArea>Department of Neural Information Processing, University of Ulm, D-89069, Ulm</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: palm@informatik.uni-ulm.de</mods:affiliation><country wicri:rule="url">Allemagne</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2001</date></imprint><idno type="ISSN">0302-9743</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">3719D6AA88CB6ED7B9605980933559F508A74577</idno><idno type="DOI">10.1007/3-540-48219-9_41</idno><idno type="ChapterID">41</idno><idno type="ChapterID">Chap41</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVM approach was originally developed for binary classification problems. In this paper SVM architectures for multi-class classification problems are discussed, in particular we consider binary trees of SVMs to solve the multi-class pattern recognition problem. Numerical results for different classifiers on a benchmark data set handwritten digits are presented.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/Istex/Curation
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002B33 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Curation/biblio.hfd -nk 002B33 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Ticri/CIDE
|area= OcrV1
|flux= Istex
|étape= Curation
|type= RBID
|clé= ISTEX:3719D6AA88CB6ED7B9605980933559F508A74577
|texte= Tree-Structured Support Vector Machines for Multi-class Pattern Recognition
}}
| This area was generated with Dilib version V0.6.32. |  |