Error bounds of multi-graph regularized semi-supervised classification
In this paper, we investigate the generalization performance of the multi-graph regularized semi-supervised classification algorithm associated with the hinge loss. We provide estimates for the excess misclassification error of multi-graph regularized classifiers and show the relations between the generalization performance and the structural invariants of data graphs. Experiments performed on real database demonstrate the effectiveness of our theoretical analysis.
semi-supervised learning tikhonov regularization reproducing kernel hilbert space rademacher complexity graph laplacian