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dc.contributor.authorVanlı, N. Denizcanen_US
dc.contributor.authorSayın, Muhammed O.en_US
dc.contributor.authorErgüt, S.en_US
dc.contributor.authorKozat, Süleyman S.en_US
dc.coverage.spatialLisbon, Portugal
dc.date.accessioned2016-02-08T11:52:54Z
dc.date.available2016-02-08T11:52:54Z
dc.date.issued2014-09en_US
dc.identifier.urihttp://hdl.handle.net/11693/27419
dc.descriptionDate of Conference: 1-5 Sept. 2014
dc.descriptionConference name: 22nd European Signal Processing Conference (EUSIPCO), 2014
dc.description.abstractWe investigate the problem of adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an individual sequence manner. We partition the regressor space using hyperplanes in a nested structure according to the notion of a tree. In this manner, we introduce an adaptive nonlinear regression algorithm that not only adapts the regressor of each partition but also learns the complete tree structure with a computational complexity only polynomial in the number of nodes of the tree. Our algorithm is constructed to directly minimize the final regression error without introducing any ad-hoc parameters. Moreover, our method can be readily incorporated with any tree construction method as demonstrated in the paper. © 2014 EURASIP.en_US
dc.language.isoEnglishen_US
dc.source.titleEuropean Signal Processing Conferenceen_US
dc.relation.isversionofhttps://ieeexplore.ieee.org/document/6952417
dc.subjectAdaptiveen_US
dc.subjectBinary treeen_US
dc.subjectNonlinear adaptive filteringen_US
dc.subjectNonlinear regressionen_US
dc.subjectSequentialen_US
dc.subjectAlgorithmsen_US
dc.subjectBinary treesen_US
dc.subjectComputational complexityen_US
dc.subjectPiecewise linear techniquesen_US
dc.subjectRegression analysisen_US
dc.subjectSignal processingen_US
dc.subjectIndividual sequencesen_US
dc.subjectNested structuresen_US
dc.subjectNon-linear regressionen_US
dc.subjectNonlinear adaptive filteringen_US
dc.subjectPiecewise linear regressionen_US
dc.subjectSequentialen_US
dc.subjectTree constructionen_US
dc.subjectTrees (mathematics)en_US
dc.titlePiecewise nonlinear regression via decision adaptive treesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage1188en_US
dc.citation.epage1192en_US
dc.publisherIEEEen_US


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