Open Access

An Improved Way to Make Large-Scale SVR Learning Practical

EURASIP Journal on Advances in Signal Processing20042004:723740

https://doi.org/10.1155/S1110865704312096

Received: 31 May 2003

Published: 8 July 2004

Abstract

We first put forward a new algorithm of reduced support vector regression (RSVR) and adopt a new approach to make a similar mathematical form as that of support vector classification. Then we describe a fast training algorithm for simplified support vector regression, sequential minimal optimization (SMO) which was used to train SVM before. Experiments prove that this new method converges considerably faster than other methods that require the presence of a substantial amount of the data in memory.

Keywords and phrases

RSVRSVMsequential minimal optimization

Authors’ Affiliations

(1)
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University

Copyright

© Yong et al. 2004