Title :
Online SVR training by solving the primal optimization problem
Author :
Brugger, D. ; Rosenstiel, W. ; Bogdan, M.
Author_Institution :
Tech. Inf., Univ. Tubingen, Tubingen, Germany
Abstract :
Online regression estimation becomes important in presence of drifts and rapid changes in the training data. In this article we propose a new online training algorithm for SVR, called PRIONA, which is based on the idea of computing approximate solutions to the primal optimization problem. We explore different unconstrained optimization methods for the solution of the primal SVR problem and investigate the impact of different buffering strategies. By using a line search PRIONA does not require a priori selection of a learning rate which facilitates its practical application. Further PRIONA is shown to perform better in terms of prediction accuracy on various benchmark data sets in comparison to the NORMA and SILK online SVR algorithms.
Keywords :
Newton method; estimation theory; optimisation; regression analysis; support vector machines; Newton descent direction; PRIONA-online training algorithm; approximate solution computation; buffering strategy; online SVR training; primal optimization problem; support vector regression estimation; Accuracy; Application software; Brain computer interfaces; Electroencephalography; Least squares approximation; Optimization methods; Stochastic processes; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on
Conference_Location :
Grenoble
Print_ISBN :
978-1-4244-4947-7
Electronic_ISBN :
978-1-4244-4948-4
DOI :
10.1109/MLSP.2009.5306234
