Presentation	2013-09-02 Topics on the Cost in Machine Learning Shotaro AKAHO,
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Abstract(in English)	The goal of most machine learning algorithms is to minimize a cost function, and thus the cost is a subject of major interest in machine learning research. Bayesian decision theory, which gives a fundamental framework of cost, assumes that the probabilities and cost values are known. However, they are unknown in practical situations, so machine learning techniques are necessary to resolve such uncertainty. In this talk, several interesting research topics on the cost are presented, including relation to privacy preservation and fairness-aware learning that have attracted much attention recently.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Cost-sensitive learning / robustness / Bayesian decision theory / ROC analysis / inverse reinforcement learning
Paper #	PRMU2013-40,IBISML2013-20
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Conference Information
Committee	PRMU
Conference Date	2013/8/26(1days)
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Paper Information
Registration To	Pattern Recognition and Media Understanding (PRMU)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Topics on the Cost in Machine Learning
Sub Title (in English)
Keyword(1)	Cost-sensitive learning
Keyword(2)	robustness
Keyword(3)	Bayesian decision theory
Keyword(4)	ROC analysis
Keyword(5)	inverse reinforcement learning
1st Author's Name	Shotaro AKAHO
1st Author's Affiliation	The National Institute of Advanced Industrial Science and Technology (AIST)()
Date	2013-09-02
Paper #	PRMU2013-40,IBISML2013-20
Volume (vol)	vol.113
Number (no)	196
Page	pp.pp.-
#Pages	2
Date of Issue