Presentation | 2002/3/11 Bayes Generalization Errors of Reduced Rank Approximation Kazuho WATANABE, Sumlo WATANABE, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Reduced rank approximation corresponds to a three-layer linear neural network with reduced hidden layer size. A lot of attention is paid to the properties of it and some basic questions can sometimes be answered analytically for its linearity. However, as is the case with other layered models such as neural networks and gaussian mixtures, its true parameter is not identifiable. Therefore some problems related to learning remain unsolved. In this paper, we analyze the generalization error of the reduced rank approximation in Bayesian estimation, and figure out its upper bound using an algebraic geometrical method. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Reduced rank approximation / Bayesian estimation / generalization error / algebraic geometry / singularities |
Paper # | NC2001-149 |
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Committee | NC |
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Conference Date | 2002/3/11(1days) |
Place (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) | Bayes Generalization Errors of Reduced Rank Approximation |
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Keyword(1) | Reduced rank approximation |
Keyword(2) | Bayesian estimation |
Keyword(3) | generalization error |
Keyword(4) | algebraic geometry |
Keyword(5) | singularities |
1st Author's Name | Kazuho WATANABE |
1st Author's Affiliation | Department of Computer Science, Tokyo Institute of Technology() |
2nd Author's Name | Sumlo WATANABE |
2nd Author's Affiliation | P&I Lab., Tokyo Institute of Technology |
Date | 2002/3/11 |
Paper # | NC2001-149 |
Volume (vol) | vol.101 |
Number (no) | 735 |
Page | pp.pp.- |
#Pages | 8 |
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