Vanlı, N. DenizcanSayın, Muhammed O.Ergüt, S.Kozat, Süleyman S.2016-02-082016-02-082014-09http://hdl.handle.net/11693/27419Date of Conference: 1-5 Sept. 2014Conference name: 22nd European Signal Processing Conference (EUSIPCO), 2014We 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.EnglishAdaptiveBinary treeNonlinear adaptive filteringNonlinear regressionSequentialAlgorithmsBinary treesComputational complexityPiecewise linear techniquesRegression analysisSignal processingIndividual sequencesNested structuresNon-linear regressionNonlinear adaptive filteringPiecewise linear regressionSequentialTree constructionTrees (mathematics)Conference Paper