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dc.contributor.authorVanli, N. D.en_US
dc.contributor.authorKozat, S. S.en_US
dc.date.accessioned2016-02-08T10:40:48Z
dc.date.available2016-02-08T10:40:48Z
dc.date.issued2014en_US
dc.identifier.issn1053-587X
dc.identifier.urihttp://hdl.handle.net/11693/25203
dc.description.abstractIn this paper, we investigate 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 use a tree notion in order to partition the space of regressors in a nested structure. The introduced algorithms adapt not only their regression functions but also the complete tree structure while achieving the performance of the 'best' linear mixture of a doubly exponential number of partitions, with a computational complexity only polynomial in the number of nodes of the tree. While constructing these algorithms, we also avoid using any artificial 'weighting' of models (with highly data dependent parameters) and, instead, directly minimize the final regression error, which is the ultimate performance goal. The introduced methods are generic such that they can readily incorporate different tree construction methods such as random trees in their framework and can use different regressor or partitioning functions as demonstrated in the paper.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Signal Processingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TSP.2014.2349882en_US
dc.subjectNonlinear regressionen_US
dc.subjectNon-linear regressionen_US
dc.subjectNonlinear adaptive filteringen_US
dc.subjectPiece-wiseen_US
dc.subjectBinary treesen_US
dc.titleA comprehensive approach to universal piecewise nonlinear regression based on treesen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage5471en_US
dc.citation.epage5486en_US
dc.citation.volumeNumber62en_US
dc.citation.issueNumber20en_US
dc.identifier.doi10.1109/TSP.2014.2349882en_US
dc.publisherIEEEen_US


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