Twin support vector machines (TWSVMs) have been shown to be effective classifiers for a range of pattern classification tasks. However, the TWSVM formulation suffers from shortcomings: (i) uses hinge loss function which renders it sensitive dataset outliers (noise sensitivity). (ii) It requires matrix inversion calculation in Wolfe-dual is intractable datasets with large numbers features/sample...

Abstract A classification problem aims at constructing a best classifier with the smallest risk. When sample size approaches infinity, learning algorithms for are characterized by an asymptotical property, i.e., universal consistency. It plays crucial role in measuring construction of rules. consistent algorithm ensures that larger is, more accurately distribution samples could be reconstructed...