A tree-based solution to nonlinear regression problem

Date
2016
Advisor
Instructor
Source Title
Proceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
1233 - 1236
Language
Turkish
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

In this paper, we offer and examine a new algorithm for sequential nonlinear regression problem. In this architecture, we use piecewise adaptive linear functions to find the nonlinear regression model sequentially. For more accurate and faster convergence, we combine a large class of piecewise linear functions. These piecewise linear functions are constructed by composing different adaptive linear functions, which are represented by the nodes of a lexicographical tree. With this tree structure, computational complexity of the algorithm is significantly reduced. To show the performance of the proposed algorithm, we present a simulation which is performed by using a well-known real data set.

Course
Other identifiers
Book Title
Keywords
On-line learning, Classification, Computational efficiency
Citation
Published Version (Please cite this version)