Presentation	1996/3/18 On the Basins of the Associative Memory Using the Boltzmann Machine Learning Tetsuya Kojima, Tsutomu Date,
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Abstract(in English)	The Hopfield neural network should be able to recall the given nominal patterns from the "noisy" input patterns in order to work well as the associative memory. The size of the basins of the nominal patterns characterizes this property well. In this study, we investigate the percentage at which the nominal patterns can be recalled perfectly from the inputs having various direction cosines with the nominal one when the Boltzmann machine learning is used. It is also shown that such percentages about the inputs with less noise increase with the number of units when the memory rate is under some critical value.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	associative memory / Boltzmann machine learning / basin of attraction
Paper #	NC-95-130
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Conference Information
Committee	NC
Conference Date	1996/3/18(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)	On the Basins of the Associative Memory Using the Boltzmann Machine Learning
Sub Title (in English)
Keyword(1)	associative memory
Keyword(2)	Boltzmann machine learning
Keyword(3)	basin of attraction
1st Author's Name	Tetsuya Kojima
1st Author's Affiliation	Graduate School of Engineering, Hokkaido University()
2nd Author's Name	Tsutomu Date
2nd Author's Affiliation	Graduate School of Engineering, Hokkaido University
Date	1996/3/18
Paper #	NC-95-130
Volume (vol)	vol.95
Number (no)	598
Page	pp.pp.-
#Pages	8
Date of Issue