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dc.contributor.advisorOkan, Öznur Taştanen_US
dc.contributor.authorOrhan, Cemen_US
dc.date.accessioned2016-08-23T07:57:39Z
dc.date.available2016-08-23T07:57:39Z
dc.date.copyright2016-08
dc.date.issued2016-08
dc.date.submitted2016-08-03
dc.identifier.urihttp://hdl.handle.net/11693/32157
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2016.en_US
dc.descriptionIncludes bibliographical references (leaves 55-63).en_US
dc.description.abstractIn many real-world machine learning applications, unlabeled data are abundant whereas the class labels are expensive and/or scarce. An active learner aims to obtain a model with high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling. We propose two novel active learning approaches for pool-based active learning setting: ALEVS for querying single example at each iteration and DBALEVS for querying a batch of examples. ALEVS and DBALEVS select the most in uential instance(s) based on statistical leverages scores of examples. The rank-k statistical leverage score of i-th row of an n x n kernel matrix K is the squared norm of the i-th row of the matrix U whose columns are the top-k eigenvectors of K. Statistical leverage scores are shown to be useful in matrix approximation algorithms in finding in uential rows of a matrix. ALEVS and DBALEVS assess the in uence of the examples by the statistical leverage scores of kernel matrix computed on the examples of the pool. Additionally, through maximizing a submodular set function at each iteration DBALEVS selects a diverse a set of examples that are highly in uential but are dissimilar to selected labeled set. Extensive experiments on diverse datasets show that the proposed methods, ALEVS and DBALEVS offer more effective strategies in comparison to other single and batch mode active learning approaches, respectively.en_US
dc.description.statementofresponsibilityby Cem Orhan.en_US
dc.format.extentxi, 65 leaves : charts.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine Learningen_US
dc.subjectActive Learningen_US
dc.subjectBinary Classificationen_US
dc.subjectStatistical Leverage Scoresen_US
dc.subjectKernel Methodsen_US
dc.titleActive learning methods based on statistical leverage scoresen_US
dc.title.alternativeİstatiksel kaldıraç değerlerine dayalı etkin öğrenme metotlarıen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB153728


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