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dc.contributor.authorGüvenir, H. A.en_US
dc.contributor.authorKoç, H. G.en_US
dc.date.accessioned2016-02-08T10:42:39Z
dc.date.available2016-02-08T10:42:39Zen_US
dc.date.issued1998en_US
dc.identifier.issn0196-9722
dc.identifier.urihttp://hdl.handle.net/11693/25312en_US
dc.description.abstractThis article presents a new form of exemplar-based learning method, based on overlapping feature intervals. In this model, a concept is represented by a collection of overlappling intervals for each feature and class. Classification with Overlapping Feature Intervals (COFI) is a particular implementation of this technique. In this incremental, inductive, and supervised learning method, the basic unit of the representation is an interval. The COFI algorithm learns the projections of the intervals in each feature dimension for each class. Initially, an interval is a point on a feature-class dimension; then it can be expanded through generalization. No specialization of intervals is done on feature-class dimensions by this algorithm. Classification in the COFI algorithm is based on a majority voting among the local predictions that are made individually by each feature. An evaluation of COFI and its comparison with similar other classification techniques is given.en_US
dc.language.isoEnglishen_US
dc.source.titleCybernetics and Systemsen_US
dc.relation.isversionofhttps://doi.org/10.1080/019697298125713en_US
dc.subjectLearning Algorithmsen_US
dc.subjectLearning Systemsen_US
dc.subjectClassification with Overlapping Feature Intervals (COFI) Algorithmen_US
dc.subjectCyberneticsen_US
dc.titleConcept representation with overlapping feature intervalsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage263en_US
dc.citation.epage282en_US
dc.citation.volumeNumber29en_US
dc.citation.issueNumber3en_US
dc.identifier.doi10.1080/019697298125713en_US
dc.publisherTaylor & Francis Inc.en_US
dc.identifier.eissn1087-6553


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