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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)
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)
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)
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)
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)
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 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)
Rough set theory was an effective tool in dealing with vague and uncertainty information. A new comprehensive evaluation model based on rough set theory was proposed in the paper, computation of weighting coefficient in evaluation model was transformed into importance of attributes in rough set theory, thus it effectively avoided subjectivity about(More)