Presentation	2001/12/14 On-Line Learning of a Simple Perceptron Learning with Margin Kazuyuki HARA, Masato OKADA,
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Abstract(in English)	We analyze a learning method using the margin κ a la Gardner for simple perceptron. The proposed method corresponds to the perceptron learning when κ=0, and it converges to the Hebbian learning when κ→∞. Neverthless, we found that the generalization ability of the present method outperforms those of the perceptron and the Hebbian methods at early stage of learning. Asymptotic property of the learning curve of the present method is analyzed by the computer simulation and it was the same as that of the perceptron learning.
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Keyword(in English)	On-line learning / Margin / Simple perceptron / Generalization ability / Perceptron learning
Paper #	NC2001-77
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Conference Information
Committee	NC
Conference Date	2001/12/14(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-Line Learning of a Simple Perceptron Learning with Margin
Sub Title (in English)
Keyword(1)	On-line learning
Keyword(2)	Margin
Keyword(3)	Simple perceptron
Keyword(4)	Generalization ability
Keyword(5)	Perceptron learning
1st Author's Name	Kazuyuki HARA
1st Author's Affiliation	Department of Electronics and Information Engineering, Tokyo Metropolitan College of Technology()
2nd Author's Name	Masato OKADA
2nd Author's Affiliation	Laboratry for Mathematical Neuroscience, Brain Science Institute, RIKEN
Date	2001/12/14
Paper #	NC2001-77
Volume (vol)	vol.101
Number (no)	534
Page	pp.pp.-
#Pages	7
Date of Issue