Name
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. |
Year | Venue | Keywords |
|---|---|---|
2014 | Signal Processing Conference | 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,Nonlinear regression,adaptive,binary tree,nonlinear adaptive filtering,sequential |
Field | DocType | ISSN |
Multivariate adaptive regression splines,Mathematical optimization,Principal component regression,Polynomial regression,Logistic model tree,Binary tree,Proper linear model,Mathematics,Piecewise,Segmented regression | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nuri Denizcan Vanli | 1 | 77 | 6.77 |
| Muhammed O. Sayin | 2 | 77 | 5.77 |
| Salih Ergüt | 3 | 365 | 19.17 |
| S. S. Kozat | 4 | 194 | 16.72 |