Twin least squares support vector regression
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- 作者：Yong-Ping Zhao ; Jing Zhao ; Min Zhao
- 关键词：Support vector machine ; Support vector regression ; Kernel method ; Least squares
- 刊名：Neurocomputing
- 出版年：2013
- 出版时间：22 October, 2013
- 年：2013
- 卷：118
- 期：Complete
- 页码：225-236
- 全文大小：682 K
文摘
In this paper, combining the spirit of twin hyperplanes with the fast speed of least squares support vector regression (LSSVR) yields a new regressor, termed as twin least squares support vector regression (TLSSVR). As a result, TLSSVR outperforms normal LSSVR in the generalization performance, and as opposed to other algorithms of twin hyperplanes, TLSSVR owns faster computational speed. When coping with large scale problems, this advantage is obvious. To accelerate the testing speed of TLSSVR, TLSSVR is sparsified using a simple mechanism, thus obtaining STLSSVR. In addition to introducing these algorithms above, a lot of experiments including a toy problem, several small and large scale data sets, and a gas furnace example are done. These applications demonstrate the effectiveness and efficiency of the proposed algorithms.