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dc.contributor.authorHsu, YTen_US
dc.contributor.authorHong, TPen_US
dc.contributor.authorTseng, SSen_US
dc.date.accessioned2014-12-08T15:43:37Z-
dc.date.available2014-12-08T15:43:37Z-
dc.date.issued2001-08-01en_US
dc.identifier.issn0924-6495en_US
dc.identifier.urihttp://dx.doi.org/10.1023/A:1017599000794en_US
dc.identifier.urihttp://hdl.handle.net/11536/29493-
dc.description.abstractMachine 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.en_US
dc.language.isoen_USen_US
dc.subjectentropyen_US
dc.subjectmachine learningen_US
dc.subjectnoiseen_US
dc.subjecttraining instanceen_US
dc.subjecttraining orderen_US
dc.subjectversion spaceen_US
dc.titleLearning concepts by arranging appropriate training orderen_US
dc.typeArticleen_US
dc.identifier.doi10.1023/A:1017599000794en_US
dc.identifier.journalMINDS AND MACHINESen_US
dc.citation.volume11en_US
dc.citation.issue3en_US
dc.citation.spage399en_US
dc.citation.epage415en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000170949200004-
dc.citation.woscount0-
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