Presentation | 2002/3/11 Precise Prediction and Knowledge Discovery in Model Selection Problem of Non-identifiable Learning Machines Koitiro NISHIUE, Sumio WATANABE, |
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Abstract(in English) | The model selection problem needs criterion to select the most suitable model among several models. The criterion of consistency is on behalf of knowledge discovery and the criterion of effectiveness is on behalf of precise prediction. These criterions are not yet clarified in non-identifiable learning machines such as neural networks or gaussian mixtures. We study the Bayesian model selection methods based on the minimization of the stochastic complexity. We formulate a hypothesis that the positive prior is appropriate for consistency and Jeffreys'prior is appropriate for effectiveness. We examine the hypothesis for the model selection problem of three layer neural networks. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Bayes estimation / Model selection problem / Stochastic complexity / Jeffreys'Prior / Consistency / Effectiveness |
Paper # | NC2001-151 |
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Committee | NC |
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Conference Date | 2002/3/11(1days) |
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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) | Precise Prediction and Knowledge Discovery in Model Selection Problem of Non-identifiable Learning Machines |
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Keyword(1) | Bayes estimation |
Keyword(2) | Model selection problem |
Keyword(3) | Stochastic complexity |
Keyword(4) | Jeffreys'Prior |
Keyword(5) | Consistency |
Keyword(6) | Effectiveness |
1st Author's Name | Koitiro NISHIUE |
1st Author's Affiliation | Department of Advanced Applied Electronics Tokyo Institute of Technology() |
2nd Author's Name | Sumio WATANABE |
2nd Author's Affiliation | P&I Lab., Tokyo Institute of Technology |
Date | 2002/3/11 |
Paper # | NC2001-151 |
Volume (vol) | vol.101 |
Number (no) | 735 |
Page | pp.pp.- |
#Pages | 8 |
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