Piecewise nonlinear regression via decision adaptive trees
Author
Vanlı, N. Denizcan
Sayın, Muhammed O.
Ergüt, S.
Kozat, Süleyman S.
Date
2014-09Source Title
European Signal Processing Conference
Publisher
IEEE
Pages
1188 - 1192
Language
English
Type
Conference PaperItem Usage Stats
85
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14
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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. © 2014 EURASIP.
Keywords
AdaptiveBinary tree
Nonlinear adaptive filtering
Nonlinear regression
Sequential
Algorithms
Binary trees
Computational complexity
Piecewise linear techniques
Regression analysis
Signal processing
Individual sequences
Nested structures
Non-linear regression
Nonlinear adaptive filtering
Piecewise linear regression
Sequential
Tree construction
Trees (mathematics)
Permalink
http://hdl.handle.net/11693/27419Published Version (Please cite this version)
https://ieeexplore.ieee.org/document/6952417Collections
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