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- Yitian Xu, Rui Guo, Laisheng Wang
- Cognitive Computation
- 2012

Twin support vector machine (TSVM) is a novel machine learning algorithm, which aims at finding two nonparallel planes for each class. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems rather than a single large one. Classical TSVM is proposed for the binary classification problem. However, multi-class… (More)

- Yitian Xu, Laisheng Wang
- FSKD
- 2005

- Yitian Xu, Xianli Pan, Zhijian Zhou, Zhiji Yang, Yuqun Zhang
- Applied Intelligence
- 2014

The least square twin support vector machine (LS-TSVM) obtains two non-parallel hyperplanes by directly solving two systems of linear equations instead of two quadratic programming problems (QPPs) as in the conventional twin support vector machine (TSVM), which makes the computational speed of LS-TSVM faster than that of the TSVM. However, LS-TSVM ignores… (More)

- Zhiquan Qi, Yitian Xu, Laisheng Wang, Ye Song
- Neurocomputing
- 2011

- Yitian Xu, Laisheng Wang
- Knowl.-Based Syst.
- 2012

Twin support vector regression (TSVR) is a new regression algorithm, which aims at finding -insensitive upand down-bound functions for the training points. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one in a classical SVR. However, the same penalties are given to the… (More)

- Yitian Xu, Laisheng Wang, Ruiyan Zhang
- Knowl.-Based Syst.
- 2011

- Yitian Xu, Laisheng Wang
- Applied Intelligence
- 2014

Twin support vector regression (TSVR) finds ϵ-insensitive up- and down-bound functions by resolving a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one as in a classical SVR, which makes its computational speed greatly improved. However the local information among samples are not exploited in TSVR. To make full use… (More)

- Yitian Xu, Jia Yu, Yuqun Zhang
- Knowl.-Based Syst.
- 2014

- Yitian Xu, Xin Lv, Zheng Wang, Laisheng Wang
- J. Inf. Sci. Eng.
- 2014

Least squares twin support vector machine (LS-TSVM) aims at resolving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single large one as in the conventional least squares support vector machine (LS-SVM), which makes the learning speed of LS-TSVM faster than that of LS-SVM. However, same penalties are given to the negative samples… (More)

- Zhiji Yang, Yitian Xu
- Neurocomputing
- 2016