Presentation	1994/9/22 A Pattern Classification Method Using Domain Knowledge and Empirical Data Kiyoshi Nakabayashi,
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Abstract(in English)	A new classification method is presented which utilizes both human experts′ domain knowledge and empirical data.Based on the Ba yesian learning concept,an equation for estimating a posteriori probability is derived in which the result is given as the weighted-sum of the estimations by the domain knowledge and the training samples.optimization procedures employing likelihood and classification rate as the optimization criteria are provided to determine the confidence factor of the domain knowledge. Experimental results demonstrate that the method achieves better performance than the one only with the domain knowledge or with the training samples.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Classification / Knowledge Acquisition / A posteriori probability / Bayesian Learning
Paper #	PRU94-27
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Conference Information
Committee	PRU
Conference Date	1994/9/22(1days)
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Paper Information
Registration To	Pattern Recognition and Understanding (PRU)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	A Pattern Classification Method Using Domain Knowledge and Empirical Data
Sub Title (in English)
Keyword(1)	Classification
Keyword(2)	Knowledge Acquisition
Keyword(3)	A posteriori probability
Keyword(4)	Bayesian Learning
1st Author's Name	Kiyoshi Nakabayashi
1st Author's Affiliation	NTT Information and Communication Systems Laboratories()
Date	1994/9/22
Paper #	PRU94-27
Volume (vol)	vol.94
Number (no)	242
Page	pp.pp.-
#Pages	8
Date of Issue