Presentation 1997/2/6
A Comparison of Laplacian Prior and Gaussian Prior for Hierarchical Bayes Learning in Neural Nets
Y. Chonan, T. Matsumoto,
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Abstract(in English) A hierarchical Bayes approach is taken in training Feedforward Neural Nets. Assuming Gaussian distribution and Laplacian distribution as prior probability of weight parameters, both models are evaluated by comparing their marginal likelihood. Simulation result shows marginal likelihood has close contacts with generalization ability.
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Keyword(in English) Hierarchical Bayes Inference / Neural Net / Gaussian Prior / Laplacian Prior / Marginal Likelihood
Paper # NLP96-132,NC96-86
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Committee NC
Conference Date 1997/2/6(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) A Comparison of Laplacian Prior and Gaussian Prior for Hierarchical Bayes Learning in Neural Nets
Sub Title (in English)
Keyword(1) Hierarchical Bayes Inference
Keyword(2) Neural Net
Keyword(3) Gaussian Prior
Keyword(4) Laplacian Prior
Keyword(5) Marginal Likelihood
1st Author's Name Y. Chonan
1st Author's Affiliation Department of Electrical, Electronics and Computer Engineering, Waseda University()
2nd Author's Name T. Matsumoto
2nd Author's Affiliation Department of Electrical, Electronics and Computer Engineering, Waseda University
Date 1997/2/6
Paper # NLP96-132,NC96-86
Volume (vol) vol.96
Number (no) 511
Page pp.pp.-
#Pages 8
Date of Issue