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.
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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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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
Date of Issue