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dc.contributor.authorHong, TPen_US
dc.contributor.authorTseng, SSen_US
dc.date.accessioned2014-12-08T15:01:55Z-
dc.date.available2014-12-08T15:01:55Z-
dc.date.issued1997-03-01en_US
dc.identifier.issn1041-4347en_US
dc.identifier.urihttp://dx.doi.org/10.1109/69.591457en_US
dc.identifier.urihttp://hdl.handle.net/11536/667-
dc.description.abstractThis paper generalizes the learning strategy of version space to manage noisy and uncertain training data. A new learning algorithm is proposed that consists of two main phases: searching and pruning. The searching phase generates and collects possible candidates into a large set; the pruning phase then prunes this set according to various criteria to find a maximally consistent version space. When the training instances cannot completely be classified, the proposed learning algorithm can make a trade-off between including positive training instances and excluding negative ones according to the requirements of different application domains. Furthermore, suitable pruning parameters are chosen according to a given time limit, so the algorithm can also make a trade-off between time complexity and accuracy. The proposed learning algorithm is then a flexible and efficient induction method that makes the version space learning strategy more practical.en_US
dc.language.isoen_USen_US
dc.subjectmachine learningen_US
dc.subjectversion spaceen_US
dc.subjectmultiple version spacesen_US
dc.subjectnoiseen_US
dc.subjectuncertaintyen_US
dc.subjecttraining instanceen_US
dc.titleA generalized version space learning algorithm for noisy and uncertain dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/69.591457en_US
dc.identifier.journalIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERINGen_US
dc.citation.volume9en_US
dc.citation.issue2en_US
dc.citation.spage336en_US
dc.citation.epage340en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:A1997WV23100012-
dc.citation.woscount33-
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