A new probabilistic induction method

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題名:	A new probabilistic induction method
作者:	R. H. Hou;T. P. Hong;S. S. Tseng;S. Y. Kuo
貢獻者:	Department of Information Science and Applications
日期:	1997
上傳時間:	2009-11-30 08:03:10 (UTC+0)
出版者:	Asia University
摘要:	Knowledge acquisition by interviewing a domain expert is one of the most problematic aspects of the development of expert systems. As an alternative, methods for inducing concept descriptions from examples have proven useful in eliminating this bottleneck. In this paper, we propose a probabilistic induction method (PIM), which is an improvement of the Chan and Wong method, for detecting relevant patterns implicit in a given data set. PIM uses the technique of residual analysis and several heuristics to effectively detect complex relevant patterns and to avoid the problem of combinatorial explosion. A reasonable trade-off between the induction time and the classification ratio is achieved. Moreover, PIM quickly classifies unknown objects using classification rules converted from the positively relevant patterns detected. Three experiments are conducted to confirm the validity of PIM.
關聯:	Journal of Automated Reasoning 18(1):5-24
顯示於類別:	[行動商務與多媒體應用學系] 期刊論文

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