Presentation	2007-11-19 Constructing error-correcting output coding using latent variable models Nobuhiko YAMAGUCHI,
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Abstract(in English)	The object of this paper is to construct a multi-class classifier by Error-Correcting Output Coding (ECOC) approach. The algorithm of the ECOC approach is divided into three steps: 1) dividing K classes into two super groups, 2) constructing multiple binary classifiers which separate the two super groups, and 3) combining the multiple binary classifiers. The ECOC approach constructs a classifier by sequentially performing the three steps. However, since these three steps are mutually related to each other, it is desirable to simultaneously perform the three steps with considering each other's steps. We therefore propose ECOC-LVM (ECOC using Latent Variable Models) which simultaneously performs the three steps.
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Keyword(in English)	Error-correcting output coding / Neural networks / Pattern classification / Latent variable models
Paper #	NC2007-70
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Conference Information
Committee	NC
Conference Date	2007/11/11(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Constructing error-correcting output coding using latent variable models
Sub Title (in English)
Keyword(1)	Error-correcting output coding
Keyword(2)	Neural networks
Keyword(3)	Pattern classification
Keyword(4)	Latent variable models
1st Author's Name	Nobuhiko YAMAGUCHI
1st Author's Affiliation	Faculty of Science and Engineering, Saga University()
Date	2007-11-19
Paper #	NC2007-70
Volume (vol)	vol.107
Number (no)	328
Page	pp.pp.-
#Pages	5
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