Piecewise nonlinear regression via decision adaptive trees
dc.citation.epage | 1192 | en_US |
dc.citation.spage | 1188 | en_US |
dc.contributor.author | Vanlı, N. Denizcan | en_US |
dc.contributor.author | Sayın, Muhammed O. | en_US |
dc.contributor.author | Ergüt, S. | en_US |
dc.contributor.author | Kozat, Süleyman S. | en_US |
dc.coverage.spatial | Lisbon, Portugal | |
dc.date.accessioned | 2016-02-08T11:52:54Z | |
dc.date.available | 2016-02-08T11:52:54Z | |
dc.date.issued | 2014-09 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 1-5 Sept. 2014 | |
dc.description | Conference name: 22nd European Signal Processing Conference (EUSIPCO), 2014 | |
dc.description.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. | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/27419 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | https://ieeexplore.ieee.org/document/6952417 | |
dc.source.title | European Signal Processing Conference | en_US |
dc.subject | Adaptive | en_US |
dc.subject | Binary tree | en_US |
dc.subject | Nonlinear adaptive filtering | en_US |
dc.subject | Nonlinear regression | en_US |
dc.subject | Sequential | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Binary trees | en_US |
dc.subject | Computational complexity | en_US |
dc.subject | Piecewise linear techniques | en_US |
dc.subject | Regression analysis | en_US |
dc.subject | Signal processing | en_US |
dc.subject | Individual sequences | en_US |
dc.subject | Nested structures | en_US |
dc.subject | Non-linear regression | en_US |
dc.subject | Nonlinear adaptive filtering | en_US |
dc.subject | Piecewise linear regression | en_US |
dc.subject | Sequential | en_US |
dc.subject | Tree construction | en_US |
dc.subject | Trees (mathematics) | en_US |
dc.title | Piecewise nonlinear regression via decision adaptive trees | en_US |
dc.type | Conference Paper | en_US |
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