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dc.contributor.advisorGüvenir, Halil Altayen_US
dc.contributor.authorŞirin, İzzeten_US
dc.date.accessioned2016-01-08T20:10:56Z
dc.date.available2016-01-08T20:10:56Z
dc.date.issued1993
dc.identifier.urihttp://hdl.handle.net/11693/17508
dc.descriptionAnkara : Department of Computer Engineering and Information Science and Institute of Engineering and Science, Bilkent Univ., 1993.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 1993.en_US
dc.descriptionIncludes bibliographical references leaves 78-81en_US
dc.description.abstractThis thesis presents a new methodology of learning from examples, based on feature partitioning. Classification by Feature Partitioning (CFP) is a particular implementation of this technique, which is an inductive, incremental, and supervised learning method. Learning in CFP is accomplished by storing the objects separately in each feature dimension as disjoint partitions of values. A partition, a basic unit of representation which is initially a point in the feature dimension, is expanded through generalization. The CFP algorithm specializes a partition by subdividing it into two subpartitions. Theoretical (with respect to PAC-model) and empirical evaluation of the CFP is presented and compared with some other similar techniques.en_US
dc.description.statementofresponsibilityŞirin, İzzeten_US
dc.format.extentxii, 88 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine learningen_US
dc.subjectinductive learningen_US
dc.subjectincremental learningen_US
dc.subjectsupervised learningen_US
dc.subjectfeature partitioningen_US
dc.subject.lccQ325.5 .S57 1993en_US
dc.subject.lcshMachine learning.en_US
dc.subject.lcshInductive learning.en_US
dc.subject.lcshSupervised learning (machine learning).en_US
dc.subject.lcshSupervised learning.en_US
dc.titleLearning with feature partitionsen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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