Analysis of Laplacian Support Vector Machines

The goal of semi-supervised learning algorithm is to effectively incorporate labeled and unlabeled data in a general-purpose learner with small misclassification error. Although there are various algorithms to implement semi-supervised learning task, the crucial issue of dependence of generalization error on the number of labeled and unlabeled data is still poorly understood. In this paper, we consider the Laplacian Support Vector Machines (LapSVMs) and establish its error analysis.