Batch learning of disjoint feature intervals

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buir.advisorGüvenir, Halil Altay
dc.contributor.authorAkkuş, Aynur
dc.date.accessioned2016-01-08T20:13:12Z
dc.date.available2016-01-08T20:13:12Z
dc.date.issued1996
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionAnkara : Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 1996.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 1996.en_US
dc.descriptionIncludes bibliographical references leaves 98-104.en_US
dc.description.abstractThis thesis presents several learning algorithms for multi-concept descriptions in the form of disjoint feature intervals, called Feature Interval Learning algorithms (FIL). These algorithms are batch supervised inductive learning algorithms, and use feature projections of the training instances for the representcition of the classification knowledge induced. These projections can be generalized into disjoint feature intervals. Therefore, the concept description learned is a set of disjoint intervals separately for each feature. The classification of an unseen instance is based on the weighted majority voting among the local predictions of features. In order to handle noisy instances, several extensions are developed by placing weights to intervals rather than features. Empirical evaluation of the FIL algorithms is presented and compared with some other similar classification algorithms. Although the FIL algorithms achieve comparable accuracies with other algorithms, their average running times are much more less than the others. This thesis also presents a new adaptation of the well-known /s-NN classification algorithm to the feature projections approach, called A:-NNFP for k-Nearest Neighbor on Feature Projections, based on a majority voting on individual classifications made by the projections of the training set on each feature and compares with the /:-NN algorithm on some real-world and cirtificial datasets.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityAkkuş, Aynuren_US
dc.format.extentxiv, 108 leavesen_US
dc.identifier.urihttp://hdl.handle.net/11693/17759
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmachine learningen_US
dc.subjectsupervised learningen_US
dc.subjectinductive learningen_US
dc.subjectbatch learningen_US
dc.subjectfeature projectionsen_US
dc.subjectvotingen_US
dc.subject.lccQA76.9.A43 A35 1996en_US
dc.subject.lcshComputer algorithms.en_US
dc.subject.lcshMachine learning.en_US
dc.subject.lcshInductive learning.en_US
dc.subject.lcshSupervised learning.en_US
dc.titleBatch learning of disjoint feature intervalsen_US
dc.typeThesisen_US

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Dept. of Computer Engineering - Master's degree