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Article: On hadamard-type output coding in multiclass learning

TitleOn hadamard-type output coding in multiclass learning
Authors
Zhang, AijunWu, Zhi LiLi, Chun HungFang, Kai Tai
KeywordsSupport vector machines
Error-correcting output codes
Multiclass learning
Hadamard matrix
Issue Date2004
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, v. 2690, p. 397-404 How to Cite?
AbstractThe error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same size. Comparative experiments based on the support vector machines are made for some real datasets from the UCI machine learning repository. © Springer-Verlag 2003.
Persistent Identifierhttp://hdl.handle.net/10722/230792
ISSN
0302-9743
2023 SCImago Journal Rankings: 0.606

 

DC FieldValueLanguage
dc.contributor.authorZhang, Aijun-
dc.contributor.authorWu, Zhi Li-
dc.contributor.authorLi, Chun Hung-
dc.contributor.authorFang, Kai Tai-
dc.date.accessioned2016-09-01T06:06:49Z-
dc.date.available2016-09-01T06:06:49Z-
dc.date.issued2004-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, v. 2690, p. 397-404-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/230792-
dc.description.abstractThe error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same size. Comparative experiments based on the support vector machines are made for some real datasets from the UCI machine learning repository. © Springer-Verlag 2003.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectSupport vector machines-
dc.subjectError-correcting output codes-
dc.subjectMulticlass learning-
dc.subjectHadamard matrix-
dc.titleOn hadamard-type output coding in multiclass learning-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-35048838623-
dc.identifier.volume2690-
dc.identifier.spage397-
dc.identifier.epage404-
dc.identifier.eissn1611-3349-
dc.identifier.issnl0302-9743-

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