Browsing by Subject "Online regression"

Now showing 1 - 2 of 2

Open Access
Boosted adaptive filters
(2017-07) Kari, Dariush
We investigate boosted online regression and propose a novel family of regression algorithms with strong theoretical bounds. In addition, we implement several variants of the proposed generic algorithm. We specifically provide theoretical bounds for the performance of our proposed algorithms that hold in a strong mathematical sense. We achieve guaranteed performance improvement over the conventional online regression methods without any statistical assumptions on the desired data or feature vectors. We demonstrate an intrinsic relationship, in terms of boosting, between the adaptive mixture-of-experts and data reuse algorithms. Furthermore, we introduce a boosting algorithm based on random updates that is significantly faster than the conventional boosting methods and other variants of our proposed algorithms while achieving an enhanced performance gain. Hence, the random updates method is specifically applicable to the fast and high dimensional streaming data. Specifically, we investigate Recursive Least Squares (RLS)-based and Least Mean Squares (LMS)-based linear regression algorithms in a mixture-of-experts setting, and provide several variants of these well known adaptation methods. Moreover, we extend the proposed algorithms to other filters. Specifically, we investigate the effect of the proposed algorithms on piecewise linear filters. Furthermore, we provide theoretical bounds for the computational complexity of our proposed algorithms. We demonstrate substantial performance gains in terms of mean square error over the constituent filters through an extensive set of benchmark real data sets and simulated examples.
Open Access
Nonlinear regression via incremental decision trees
(Elsevier, 2019) Vanlı, N.; Sayın, M.; Neyshabouri, Mohammadreza Mohaghegh; Özkan, H.; Kozat, Süleyman S.
We study sequential nonlinear regression and introduce an online algorithm that elegantly mitigates, via an adaptively incremental hierarchical structure, convergence and undertraining issues of conventional nonlinear regression methods. Particularly, we present a piecewise linear (or nonlinear) regression algorithm that partitions the regressor space and learns a linear model at each region to combine. Unlike the conventional approaches, our algorithm effectively learns the optimal regressor space partition with the desired complexity in a completely sequential and data driven manner. Our algorithm sequentially and asymptotically achieves the performance of the optimal twice differentiable regression function for any data sequence without any statistical assumptions. The introduced algorithm can be efficiently implemented with a computational complexity that is only logarithmic in the length of data. In our experiments, we demonstrate significant gains for the well-known benchmark real data sets when compared to the state-of-the-art techniques.