標題: Learning concepts by arranging appropriate training order
作者: Hsu, YT
Hong, TP
Tseng, SS
資訊工程學系
Department of Computer Science
關鍵字: entropy;machine learning;noise;training instance;training order;version space
公開日期: 1-Aug-2001
摘要: Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used here for machine learning and an order arrangement scheme is proposed. The second step is to modify a conventional noise-free learning algorithm, thus making it suitable for noisy environment. The generalized version space learning algorithm is then adopted to process the training instances for deriving good concepts. Finally, experiments on the Iris Flower problem show that the new scheme can produce a good training order, allowing the generalized version space algorithm to have a satisfactory learning result.
URI: http://dx.doi.org/10.1023/A:1017599000794
http://hdl.handle.net/11536/29493
ISSN: 0924-6495
DOI: 10.1023/A:1017599000794
期刊: MINDS AND MACHINES
Volume: 11
Issue: 3
起始頁: 399
結束頁: 415
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