Publication:
Competitive and online piecewise linear classification

dc.contributor.coauthorÖzkan, Hüseyin
dc.contributor.coauthorPelvan, Özgün S.
dc.contributor.coauthorAkman, Arda
dc.contributor.coauthorKozat, Süleyman S.
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorDönmez, Mehmet Ali
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-09T23:04:29Z
dc.date.issued2013
dc.description.abstractIn this paper, we study the binary classification problem in machine learning and introduce a novel classification algorithm based on the 'Context Tree Weighting Method'. The introduced algorithm incrementally learns a classification model through sequential updates in the course of a given data stream, i.e., each data point is processed only once and forgotten after the classifier is updated, and asymptotically achieves the performance of the best piecewise linear classifiers defined by the 'context tree'. Since the computational complexity is only linear in the depth of the context tree, our algorithm is highly scalable and appropriate for real time processing. We present experimental results on several benchmark data sets and demonstrate that our method provides significant computational improvement both in the test (5 ∼ 35×) and training phases (40 ∼ 1000×), while achieving high classification accuracy in comparison to the SVM with RBF kernel.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipIEE Signal Processing Society
dc.identifier.doi10.1109/ICASSP.2013.6638299
dc.identifier.isbn9781-4799-0356-6
dc.identifier.issn1520-6149
dc.identifier.scopus2-s2.0-84890463116
dc.identifier.urihttps://doi.org/10.1109/ICASSP.2013.6638299
dc.identifier.urihttps://hdl.handle.net/20.500.14288/8647
dc.identifier.wos329611503122
dc.keywordsOnline
dc.keywordsCompetitive
dc.keywordsClassification
dc.keywordsPiecewise linear
dc.keywordsContext tree
dc.keywordsLDA
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
dc.subjectAcoustics
dc.subjectElectrical electronics engineering
dc.titleCompetitive and online piecewise linear classification
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorDönmez, Mehmet Ali
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit2Graduate School of Sciences and Engineering
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