Presentation	2005-09-21 Semi-Supervised Pattern Classification Based on Connective Distance Kohei INOUE, Kiichi URAHAMA,
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Abstract(in English)	The connective distance which is an example of ultra metric distances has been utilized for extracting arbitrarily shaped clusters. The connective distance has much computational complexity. Therefore, it is difficult to use it for pattern classification which needs fast computation in on-line processing. In this paper, we present a semi-supervised pattern classification method on the basis of the connective distance. We also present a speeding-up technique for the proposed method. The effectiveness of the present method is experimentally verified with an example of face recognition task.
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Keyword(in English)	semi-supervised pattern classification / connective distance / face recognition
Paper #	NLC2005-25,PRMU2005-52
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Committee	NLC
Conference Date	2005/9/14(1days)
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Paper Information
Registration To	Natural Language Understanding and Models of Communication (NLC)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	Semi-Supervised Pattern Classification Based on Connective Distance
Sub Title (in English)
Keyword(1)	semi-supervised pattern classification
Keyword(2)	connective distance
Keyword(3)	face recognition
1st Author's Name	Kohei INOUE
1st Author's Affiliation	Faculty of Design, Kyushu University()
2nd Author's Name	Kiichi URAHAMA
2nd Author's Affiliation	Faculty of Design, Kyushu University
Date	2005-09-21
Paper #	NLC2005-25,PRMU2005-52
Volume (vol)	vol.105
Number (no)	299
Page	pp.pp.-
#Pages	4
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