Neural Processing Letters

, Volume 21, Issue 3, pp 175–188

An Incremental Learning Strategy for Support Vector Regression

Authors

    • Institute of System Engineering, Faculty of Computer and Information TechnologyShanxi University
Article

DOI: 10.1007/s11063-004-5714-1

Cite this article as:
WANG, W. Neural Process Lett (2005) 21: 175. doi:10.1007/s11063-004-5714-1

Abstract

Support vector machine (SVM) provides good generalization performance but suffers from a large amount of computation. This paper presents an incremental learning strategy for support vector regression (SVR). The new method firstly formulates an explicit expression of ||W||2 by constructing an orthogonal basis in feature space together with a basic Hilbert space identity, and then finds the regression function through minimizing the formula of ||W||2 rather than solving a convex programming problem. Particularly, we combine the minimization of ||W||2 with kernel selection that can lead to good generalization performance. The presented method not only provides a novel way for incremental SVR learning, but opens an opportunity for model selection of SVR as well. An artificial data set, a benchmark data set and a real-world data set are employed to evaluate the method. The simulations support the feasibility and effectiveness of the proposed approach.

Keywords

incremental learning kernel selection regression support vector machine

Copyright information

© Springer 2005