ν -twin support vector machine with Universum data for classification

novel ν -twin support vector machine with Universum data ( 𝔘_ν -TSVM) is proposed in this paper. 𝔘_ν -TSVM allows to incorporate the prior knowledge embedded in the unlabeled samples into the supervised learning. It aims to utilize these prior knowledge to improve the generalization performance. Different from the conventional 𝔘 -SVM, 𝔘_ν -TSVM employs two Hinge loss functions to make the Universum data lie in a nonparallel insensitive loss tube, which makes it exploit these prior knowledge more flexibly. In addition, the newly introduced parameters ν 1 , ν 2 in the 𝔘_ν -TSVM have better theoretical interpretation than the penalty factor c in the 𝔘 -TSVM. Numerical experiments on seventeen benchmark datasets, handwritten digit recognition, and gender classification indicate that the Universum indeed contributes to improving the prediction accuracy. Moreover, our 𝔘_ν -TSVM is far superior to the other three algorithms ( 𝔘 -SVM, ν -TSVM and 𝔘 -TSVM) from the prediction accuracy.