In the last few years, due to the growing ubiquity of unlabeled data, much effort has been
spent by the machine learning community to develop better understanding and improve the
quality of classifiers exploiting unlabeled data. Following the manifold regularization
approach, Laplacian Support Vector Machines (LapSVMs) have shown the state of the art
performance in semi-supervised classification. In this paper we present two strategies to
solve the primal LapSVM problem, in order to overcome some issues of the original dual …

In the last few years, due to the growing ubiquity of unlabeled data, much effort has been
spent by the machine learning community to develop better understanding and improve the
quality of classifiers exploiting unlabeled data. Following the manifold regularization
approach, Laplacian Support Vector Machines (LapSVMs) have shown the state of the art
performance in semi--supervised classification. In this paper we present two strategies to
solve the primal LapSVM problem, in order to overcome some issues of the original dual …