Name
Abstract | ||
|---|---|---|
In this paper, we consider the problem of sequential nonlinear regression and introduce an efficient learning algorithm using context trees. Specifically, the regressor space is partitioned and the resulting regions are represented by a context tree. In each region, we assign an independent regression algorithm and the outputs of the all possible nonlinear models defined on the context tree are adaptively combined with a computational complexity linear in the number of nodes. The upper bounds on the performance of the algorithm are also investigated without making any statistical assumptions on the data. A numerical example is provided to illustrate the theoretical results. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SIU.2014.6830617 | Signal Processing and Communications Applications Conference |
Keywords | Field | DocType |
computational complexity,regression analysis,trees (mathematics),computational complexity,context trees,learning algorithm,regression algorithm,regressor space,sequential nonlinear regression,upper bounds,adaptive,context tree,nonlinear regression,sequential | Signal processing,Mathematical optimization,Nonlinear system,Pattern recognition,Regression,Regression analysis,Computer science,Regression tree analysis,Nonlinear regression,Artificial intelligence,Statistical assumption,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
2165-0608 | 0 | 0.34 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nuri Denizcan Vanli | 1 | 77 | 6.77 |
| S. S. Kozat | 2 | 194 | 16.72 |