Nowadays, learning simulation models with unlabeled data becomes a hot spot in data mining and machine learning fields. Among various kinds of approaches, semi-supervised learning (SSL) has played a more and more important role. In this paper, we proposed a novel method called LapNPSVM for binary classification under SSL scenario. One of the main merits is that our method avoids inversion matrix in objective function compared with laplacian SVM and other twin SVM based approaches which is obviously an big obstacle for large scale application. Experiments on artificial and real world datasets show the generalization and speed effectiveness of our method.
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