An improved way tomake large-scale SVR learning practical

Authors:
Quan Yong;Yang Jie;Yao Lixiu;Ye Chenzhou
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

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Abstract

We first put forward a new algorithm of reduced support vector regression (RSVR) and adopt a new approach to make a similar mathematical formas that of support vector classification. Then we describe a fast training algorithm for simplified support vector regression, sequentialminimal 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.