Presentation	2001/2/2 The Generalized Loss Function using α-likelihood and its Learning Hiroyuki SHIOYA, Tsutomu DA-TE,
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Abstract(in English)	The method of the minimization of square error has been widely used for the learning of input-output systems. In this study, we propose the specialized information divergence concerning to the stochastic modeling for an input-output system. We show the learning algorithm from the minimization of its divergence and we derive the loss function with respect to the gradient of our learning algorithm inversely and mention some properties of the function. In addition, we mention that its divergence measure involves the Tsallis entropy.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Minimum square error / α-likelihood function / generalized loss function
Paper #	NC2000-93
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Conference Information
Committee	NC
Conference Date	2001/2/2(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)	The Generalized Loss Function using α-likelihood and its Learning
Sub Title (in English)
Keyword(1)	Minimum square error
Keyword(2)	α-likelihood function
Keyword(3)	generalized loss function
1st Author's Name	Hiroyuki SHIOYA
1st Author's Affiliation	Graduate School of Engineering, Hokkaido University()
2nd Author's Name	Tsutomu DA-TE
2nd Author's Affiliation	Graduate School of Engineering, Hokkaido University
Date	2001/2/2
Paper #	NC2000-93
Volume (vol)	vol.100
Number (no)	618
Page	pp.pp.-
#Pages	7
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