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dc.contributor.authorWang, CHen_US
dc.contributor.authorTsai, CRen_US
dc.contributor.authorHong, TPen_US
dc.contributor.authorTseng, SSen_US
dc.date.accessioned2014-12-08T15:41:17Z-
dc.date.available2014-12-08T15:41:17Z-
dc.date.issued2003-03-01en_US
dc.identifier.issn0924-669Xen_US
dc.identifier.urihttp://dx.doi.org/10.1023/A:1021938425987en_US
dc.identifier.urihttp://hdl.handle.net/11536/28082-
dc.description.abstractIn real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. Design of learning methods for working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the AQR learning strategy is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy linguistic rules from "soft" instances. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms.en_US
dc.language.isoen_USen_US
dc.subjectAQR algorithmen_US
dc.subjectfuzzy classificationen_US
dc.subjectfuzzy inductive learningen_US
dc.subjectmachine learningen_US
dc.subjectsoft instancesen_US
dc.titleFuzzy inductive learning strategiesen_US
dc.typeArticleen_US
dc.identifier.doi10.1023/A:1021938425987en_US
dc.identifier.journalAPPLIED INTELLIGENCEen_US
dc.citation.volume18en_US
dc.citation.issue2en_US
dc.citation.spage179en_US
dc.citation.epage193en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000180391800004-
dc.citation.woscount3-
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