Presentation	2001/6/22 Model selection by Boltzmann machine : Stochastic model and expectation model Tetsuo Furukawa,
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Abstract(in English)	A data classification method using a Boltzmann-machine algorithm is proposed in this paper. The purpose of the classification is to determine local submodels which describe the data of each class. The Boltzmann machine is enabled to estimate both classification and submodel parameters at the same time. The method is also used in conjunction with a set of multilayer-perceptron class models, in which the relevant algorithm work on the basis of calculated expectations, rather than actual stochastic behaviors. The network is shown to be successful for classification of iris data and wine data.
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Keyword(in English)	model selection / boltzman machine / modular network / PCA
Paper #	NC2001-30
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Conference Information
Committee	NC
Conference Date	2001/6/22(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Model selection by Boltzmann machine : Stochastic model and expectation model
Sub Title (in English)
Keyword(1)	model selection
Keyword(2)	boltzman machine
Keyword(3)	modular network
Keyword(4)	PCA
1st Author's Name	Tetsuo Furukawa
1st Author's Affiliation	Kyushu Institute of Technology()
Date	2001/6/22
Paper #	NC2001-30
Volume (vol)	vol.101
Number (no)	154
Page	pp.pp.-
#Pages	8
Date of Issue