DocumentCode :
148788
Title :
Piecewise nonlinear regression via decision adaptive trees
Author :
Vanli, Nuri Denizcan ; Sayin, Muhammed O. ; Ergut, Salih ; Kozat, Suleyman S.
Author_Institution :
Dept. of Electr. & Electron. Eng., Bilkent Univ., Ankara, Turkey
fYear :
2014
fDate :
1-5 Sept. 2014
Firstpage :
1188
Lastpage :
1192
Abstract :
We 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.
Keywords :
computational complexity; decision trees; piecewise linear techniques; regression analysis; ad-hoc parameters; adaptive nonlinear regression; computational complexity; decision adaptive trees; hyperplanes; piecewise nonlinear regression; regression error; regressor space; tree based piecewise linear regression algorithms; tree construction method; tree structure; Abstracts; Filtering algorithms; Linear regression; Radio access networks; Three-dimensional displays; Nonlinear regression; adaptive; binary tree; nonlinear adaptive filtering; sequential;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European
Conference_Location :
Lisbon
Type :
conf
Filename :
6952417
Link To Document :
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