An Approach for Incremental Semi-supervised SVM
Wael Emara, Mehmed M. Kantardzic, Kai-Uwe Sattler, Dirk Habich, Wolfgang Lehner · 2007
In this paper we propose an approach for incremental learning of semi-supervised SVM. The proposed approach makes use of the locality of radial basis function kernels to do local and incremental training of semi-supervised support vector machines. The algorithm introduces a se- quential minimal optimization based implementation of the branch and bound technique for training semi-supervised SVM problems. The novelty of our approach lies in the