Adaptive hierarchical space partitioning for online classification

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dc.citation.epage2294en_US
dc.citation.spage2290en_US
dc.contributor.authorKılıç, O. Fatihen_US
dc.contributor.authorVanlı, N. D.en_US
dc.contributor.authorÖzkan, H.en_US
dc.contributor.authorDelibalta, İ.en_US
dc.contributor.authorKozat, Süleyman Serdaren_US
dc.coverage.spatialBudapest, Hungaryen_US
dc.date.accessioned2018-04-12T11:49:40Z
dc.date.available2018-04-12T11:49:40Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 29 August-2 September 2016en_US
dc.descriptionConference Name: 24th European Signal Processing Conference, EUSIPCO 2016en_US
dc.description.abstractWe propose an online algorithm for supervised learning with strong performance guarantees under the empirical zero-one loss. The proposed method adaptively partitions the feature space in a hierarchical manner and generates a powerful finite combination of basic models. This provides algorithm to obtain a strong classification method which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:49:40Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1109/EUSIPCO.2016.7760657en_US
dc.identifier.issn2219-5491en_US
dc.identifier.urihttp://hdl.handle.net/11693/37739
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/EUSIPCO.2016.7760657en_US
dc.source.titleProceedings of the 24th European Signal Processing Conference, EUSIPCO 2016en_US
dc.subjectArtificial intelligenceen_US
dc.subjectLearning algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectClassification methodsen_US
dc.subjectClassifier modelsen_US
dc.subjectEnsemble techniquesen_US
dc.subjectOn-line algorithmsen_US
dc.subjectOn-line classificationen_US
dc.subjectPerformance guaranteesen_US
dc.subjectSpace partitioningen_US
dc.subjectState of the arten_US
dc.subjectSignal processingen_US
dc.titleAdaptive hierarchical space partitioning for online classificationen_US
dc.typeConference Paperen_US

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