An Improved Way to Make Large-Scale SVR Learning Practical
EURASIP Journal on Advances in Signal Processing volume 2004, Article number: 723740 (2004)
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.
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Yong, Q., Jie, Y., Lixiu, Y. et al. An Improved Way to Make Large-Scale SVR Learning Practical. EURASIP J. Adv. Signal Process. 2004, 723740 (2004). https://doi.org/10.1155/S1110865704312096
Keywords and phrases
- sequential minimal optimization