Online estimation using semi-supervised least square SVR

Abstract

Online least square support vector regression (LS-SVR) is an extension from a standard SVR for fast learning. In this paper, we consider a combination of the online LS-SVR and semi-supervised learning in order to boost estimation accuracy. The semi-supervised learning is useful for real-time and complex estimation applications because it uses a small amount of the labeled data but sufficient unlabeled data that can be easily obtained. The algorithms are evaluated for two experiments, i.e. state estimation of a robot arm and forecasting of chaotic time-series. The experimental results show that the proposed algorithm yields more accurate estimation than the compared online LS-SVR.