Presentation 2009-03-11
On a Relation between a Limit Theorem in Learning Theory and Singular Fluctuation
Sumio WATANABE,
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Abstract(in English) Let B_g, B_t, G_g, G_t, and V be the Bayes generalization error, the Bayes training error, the Gibbs generalization error, the Gibbs training error, and the functional variance, respectively. If the α posteriori distribution with the inverse temperature β>0 is employed, then the equations of states in learning, E[B_g]=E[B_t]+βE[V] and E[G_g]=E[G_t]+βE[V], hold. Hence B_t+βV and G_t+βV can be applied to model selection and hyperparameter optimization. In the previous papers, we proved these equations on the assumption that the true distribution is contained in the learning machine and that Fisher information matrix is singular. In this paper, we prove the same equations hold on the assumption that the true distribution is not contained in the learning machine and that Fisher information matrix is positive definite. Also we show nV converges to a constant in probability.
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Keyword(in English) Singular learning machines / Bayes generalization error / Bayes training error / Gibbs generalization error / Gibbs training error
Paper # NC2008-111
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Committee NC
Conference Date 2009/3/4(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) On a Relation between a Limit Theorem in Learning Theory and Singular Fluctuation
Sub Title (in English)
Keyword(1) Singular learning machines
Keyword(2) Bayes generalization error
Keyword(3) Bayes training error
Keyword(4) Gibbs generalization error
Keyword(5) Gibbs training error
1st Author's Name Sumio WATANABE
1st Author's Affiliation Tokyo Institute of Technology, PI Lab()
Date 2009-03-11
Paper # NC2008-111
Volume (vol) vol.108
Number (no) 480
Page pp.pp.-
#Pages 6
Date of Issue