Presentation	2013-12-21 Difference of Enough Numbers for General and Regular Asymptotic Theories in Statistical Learning Sumio WATANABE,
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Abstract(in English)	There are two asymptotic theories in statistical learning. One is the regular theory which assumes that the likelihood function can be approximated by a normal distribution, the other is the general theory which does not need such assumption. Prom the mathematical point of view, both theories need the limit condition that the number of traning samples goes to infinity. However, it strongly depends on the situation whether we can apply the theories to a practical problem or not. In this paper, we study the problem of discovery, and experimentally show that the regular theory can not be applied for observation of discovery of structure, whereas the general theory can be applicable.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	General asymptotic theory / Regular asymptotic theory / WAIC / Sample Number
Paper #	NC2013-61
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Conference Information
Committee	NC
Conference Date	2013/12/14(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)	Difference of Enough Numbers for General and Regular Asymptotic Theories in Statistical Learning
Sub Title (in English)
Keyword(1)	General asymptotic theory
Keyword(2)	Regular asymptotic theory
Keyword(3)	WAIC
Keyword(4)	Sample Number
1st Author's Name	Sumio WATANABE
1st Author's Affiliation	Tokyo Institute of Technology()
Date	2013-12-21
Paper #	NC2013-61
Volume (vol)	vol.113
Number (no)	374
Page	pp.pp.-
#Pages	6
Date of Issue