Presentation 2003/7/21
Resolution of singularities and its application to learning theory
Miki AOYAGI, Sumio WATANABE,
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Abstract(in English) Our purpose in this paper is to give an introduction to algebraic geometry and especially construction of the blowing-up. This construction is the main tool in the resolution of singularities of an algebraic variety. By using the blowing-up process, it can be calculated the poles of a zeta function which is the integral of the Kullback distance and a certain priori probability density funciton. Hierarchical learning machines such as layered neural networks and Gaussian mixtures are non-regular learning machines. It was proved that the largest pole of the zeta function asymptotically gives the stochastic complexity of non-regular learning machine (see[1]~[3]).
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Keyword(in English) stochastic complexity / layered neural networks / non-regular learning machines / Bayesian estimate / resolution of singularities
Paper # NC2003-26
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Committee NC
Conference Date 2003/7/21(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) Resolution of singularities and its application to learning theory
Sub Title (in English)
Keyword(1) stochastic complexity
Keyword(2) layered neural networks
Keyword(3) non-regular learning machines
Keyword(4) Bayesian estimate
Keyword(5) resolution of singularities
1st Author's Name Miki AOYAGI
1st Author's Affiliation Faculty of Science and Technology, Sophia University()
2nd Author's Name Sumio WATANABE
2nd Author's Affiliation Precision and Intelligence Laboratory, Tokyo Institute of Technology
Date 2003/7/21
Paper # NC2003-26
Volume (vol) vol.103
Number (no) 227
Page pp.pp.-
#Pages 6
Date of Issue