Presentation	2005/7/20 A Model Selection Method in Singular Learning Machines Keisuke YAMAZAKI, Kenji NAGATA, Sumio WATANABE,
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Abstract(in English)	In the information engineering field, many practical learning machines, e.g. neural networks, mixture models and hidden Markov models, have been developed. In spite of their wide-range applications, there is no theoretical method to select the optimal sized model in such models i.e. singular models. Recent years, an approach to analyze the singular models was established based on algebraic geometry. In this paper, we propose a new model selection criterion, Singular Information Criterion (SingIC), based on the algebraic geometrical method.
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Keyword(in English)	Model selection / Singular learning machines / Algebraic geometry
Paper #	NC2005-31
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Conference Information
Committee	NC
Conference Date	2005/7/20(1days)
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Registration To	Neurocomputing (NC)
Language	ENG
Title (in Japanese)	(See Japanese page)
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Title (in English)	A Model Selection Method in Singular Learning Machines
Sub Title (in English)
Keyword(1)	Model selection
Keyword(2)	Singular learning machines
Keyword(3)	Algebraic geometry
1st Author's Name	Keisuke YAMAZAKI
1st Author's Affiliation	P&I Lab., Tokyo Institute of Technology()
2nd Author's Name	Kenji NAGATA
2nd Author's Affiliation	Dept. of Computational Intelligence and Systems Science, Tokyo Institute of Technology
3rd Author's Name	Sumio WATANABE
3rd Author's Affiliation	P&I Lab., Tokyo Institute of Technology
Date	2005/7/20
Paper #	NC2005-31
Volume (vol)	vol.105
Number (no)	211
Page	pp.pp.-
#Pages	6
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