Nonlinear regression using second order methods
| Autor: | Ibrahim Delibalta, Burak C. Civek, Suleyman S. Kozat |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Polynomial regression
Mathematical optimization Proper linear model Multivariate adaptive regression splines Newton Linear model 020206 networking & telecommunications 02 engineering and technology Overfitting Nonparametric regression Nonlinear regression 0202 electrical engineering electronic engineering information engineering Principal component regression 020201 artificial intelligence & image processing Piecewise linear model Mathematics |
| Zdroj: | SIU Proceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 |
| Popis: | Date of Conference: 16-19 May 2016 Conference Name: IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 We present a highly efficient algorithm for the online nonlinear regression problem. We process only the currently available data and do not reuse it, hence, there is no need for storage. For the nonlinear regression, we use piecewise linear modeling, where the regression space is partitioned into several regions and a linear model is fit to each region. As the first time in the literature, we use second order methods, e.g. Newton-Raphson Methods, and adaptively train both the region boundaries and the corresponding linear models. Therefore, we overcome the well known overfitting and underfitting problems. The proposed algorithm provides a substantial improvement in the performance compared to the state of the art. |
| Databáze: | OpenAIRE |
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