Presentation	2010-01-21 Superfast Probabilistic Classifier Masashi SUGIYAMA,
PDF Download Page	PDF download Page Link
Abstract(in Japanese)	(See Japanese page)
Abstract(in English)	Kernel logistic regression (KLR) is a powerful and flexible classification algorithm, which possesses an ability to provide the confidence of class prediction. However, its training-typically carried out by Newton's method or quasi-Newton methods-is rather time-consuming. In this paper, we propose an alternative probabilistic classification algorithm called Least-Squares Probabilistic Classifier (LSPC). The solution of LSPC can be computed analytically just by solving a system of linear equations, so LSPC is computationally efficient and stable. Through experiments, we show that the computation time of LSPC is faster than that of KLR by the factor 100 with comparable accuracy.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	probabilistic classification / kernel logistic regression / class-posterior probability / squared-loss
Paper #	CQ2009-74,PRMU2009-173,SP2009-114,MVE2009-96
Date of Issue

Conference Information
Committee	CQ
Conference Date	2010/1/14(1days)
Place (in Japanese)	(See Japanese page)
Place (in English)
Topics (in Japanese)	(See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To	Communication Quality (CQ)
Language	ENG
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Superfast Probabilistic Classifier
Sub Title (in English)
Keyword(1)	probabilistic classification
Keyword(2)	kernel logistic regression
Keyword(3)	class-posterior probability
Keyword(4)	squared-loss
1st Author's Name	Masashi SUGIYAMA
1st Author's Affiliation	Department of Computer Science, Tokyo Institute of Technology:JST()
Date	2010-01-21
Paper #	CQ2009-74,PRMU2009-173,SP2009-114,MVE2009-96
Volume (vol)	vol.109
Number (no)	373
Page	pp.pp.-
#Pages	6
Date of Issue