A multi-classification algorithm of semi-supervised support vector data description based on pairwise constraints

Abstract

A constraint-based semi-supervised support vector machine classification learning algorithm is proposed based on support vector data description algorithm with pairs of semi-supervised learning thinking combined. Multiple hyperspheres are constructed by constraints for the k-classification problems, so that the original problem converted to a k-classification problem. The algorithm to get positive constraints label and negative constraints label by calculating the degree -membership of unlabeled samples, then multiple hyperspheres constructed based on the multi-classification algorithm. Finally, simulation experiments on artificial datasets and UCI datasets to verify the effectiveness of the algorithm. © 2013 Springer-Verlag.