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 Japanese) | (See Japanese page) |
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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Committee | NC |
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Conference Date | 2001/2/2(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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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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