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      • Department of Electrical and Electronics Engineering
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      Piecewise nonlinear regression via decision adaptive trees

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      Author
      Vanlı, N. Denizcan
      Sayın, Muhammed O.
      Ergüt, S.
      Kozat, Süleyman S.
      Date
      2014-09
      Source Title
      European Signal Processing Conference
      Publisher
      IEEE
      Pages
      1188 - 1192
      Language
      English
      Type
      Conference Paper
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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
      Adaptive
      Binary 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/11693/27419
      Published Version (Please cite this version)
      https://ieeexplore.ieee.org/document/6952417
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      • Department of Electrical and Electronics Engineering 3524
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