Semi-supervised learning for weighted LS-SVM
The least squares support vector machine (LS-SVM) is an interesting variant of the SVM. It performs structural risk through margin-maximization and has excellent power of generalization. For some applications, it is more interesting to use the weighted LS-SVM where the impact of each training sample is controlled by weighting factors. In this paper, we consider the use of the weighted LS-SVM in semi-supervised learning. We propose an algorithm to perform this type of learning by extending the transductive SVM idea. We tested our algorithm on both artificial and real problems and demonstrate its usefulness comparing with other semi-supervised learning methods.