Guvenir, H. A.Şirin, İ.2016-02-082016-02-0819960885-6125http://hdl.handle.net/11693/25720This 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,.EnglishExemplar-Based LearningFeature PartitioningIncremental LearningVotingFeature ExtractionKnowledge RepresentationPattern RecognitionClassification by Feature Partitioning (CFP)Learning SystemsArticle10.1023/A:10180903172101573-0565