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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, 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)

- Yitian Xu, Laisheng Wang, Ping Zhong
- Neural Computing and Applications
- 2011

Twin support vector machine (TSVM) is a new 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 (QPPs) rather than a single large one. However, when constructing the classification plane for one class, a large number of samples… (More)

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

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

Keywords: SVR TSVR Up-and down-bound functions Weighted coefficient Weighted TSVR a b s t r a c t Twin support vector regression (TSVR) is a new regression algorithm, which aims at finding-insensitive up-and 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)… (More)

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

- Pengcheng Bu, Lihua Wang, +16 authors Xiling Shen
- Nature communications
- 2015

As patient survival drops precipitously from early-stage cancers to late-stage and metastatic cancers, microRNAs that promote relapse and metastasis can serve as prognostic and predictive markers as well as therapeutic targets for chemoprevention. Here we show that miR-1269a promotes colorectal cancer (CRC) metastasis and forms a positive feedback loop with… (More)

- Ping Zhong, Yitian Xu, Yaohong Zhao
- Neural Computing and Applications
- 2011

This paper improves the recently proposed twin support vector regression (TSVR) by formulating it as a pair of linear programming problems instead of quadratic programming problems. The use of 1-norm distance in the linear programming TSVR as opposed to the square of the 2-norm in the quadratic programming TSVR leads to the better generalization performance… (More)

Twin support vector machine (TSVM) is regarded as a milestone in the development of the powerful SVM. It finds two nonparallel planes by resolving a pair of smaller-sized quadratic programming problems rather than a single large one, which makes the learning speed of TSVM approximately four times faster than that of the standard SVM. However, the empirical… (More)