Yitian Xu

Learn More
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)
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)
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)
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)