TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition

A novel twin parametric-margin support vector machine (TPMSVM) for classification is proposed in this paper. This TPMSVM, in the spirit of the twin support vector machine (TWSVM), determines indirectly the separating hyperplane through a pair of nonparallel parametric-margin hyperplanes solved by two smaller sized support vector machine (SVM)-type problems. Similar to the parametric-margin ν‐supportν‐support vector machine (par-ν‐SVMν‐SVM), this TPMSVM is suitable for many cases, especially when the data has heteroscedastic error structure, that is, the noise strongly depends on the input value. But there is an advantage in the learning speed compared with the par-ν‐SVMν‐SVM. The experimental results on several artificial and benchmark datasets indicate that the TPMSVM not only obtains fast learning speed, but also shows good generalization.

► A twin parametric-margin support vector machine (TPMSVM) classifier is proposed.
► The TPMSVM is suitable for data with heteroscedastic error structure.
► The TPMSVM has faster learning speed than classical SVMs.
► The TPMSVM obtains comparable generalization.