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dc.contributor.authorGuvenir, H. A.en_US
dc.contributor.authorŞirin, İ.en_US
dc.date.accessioned2016-02-08T10:49:31Z
dc.date.available2016-02-08T10:49:31Zen_US
dc.date.issued1996en_US
dc.identifier.issn0885-6125
dc.identifier.urihttp://hdl.handle.net/11693/25720en_US
dc.description.abstractThis paper presents a new form of exemplar-based learning, based on a representation scheme called jfaliirf parluinning, and a panitular implementation of this technique called CFF (for Classification by feature Partioning). Learning in CFP is accomplished by storing the objects separately in each (tenure dimension as disjoint sets of values called segments A segment is; expanded through generalization or specialized by dividing in into sub-segments. Cklassification is based on a weighted voting among the individual productions of the features, which are simply the class values of the segments corresponding to the values of a test instance fur each feature An empirical evaluation of CFP and its comparison with two other classification techniques, lhai consider each feature separately are given. © 1996 Kluwer Academic Publishers,.en_US
dc.language.isoEnglishen_US
dc.source.titleMachine Learningen_US
dc.relation.isversionofhttps://doi.org/10.1023/A:1018090317210en_US
dc.subjectExemplar-Based Learningen_US
dc.subjectFeature Partitioningen_US
dc.subjectIncremental Learningen_US
dc.subjectVotingen_US
dc.subjectFeature Extractionen_US
dc.subjectKnowledge Representationen_US
dc.subjectPattern Recognitionen_US
dc.subjectClassification by Feature Partitioning (CFP)en_US
dc.subjectLearning Systemsen_US
dc.titleClassification by feature partitioningen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage47en_US
dc.citation.epage67en_US
dc.citation.volumeNumber23en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1023/A:1018090317210en_US
dc.publisherSpringer/en_US
dc.publisherKluwer Academic Publishers-Plenum Publishersen_US
dc.identifier.eissn1573-0565


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