Abstract
In this paper, a novel inductive support vector machine for semi-supervised learning, named IS3VM, is proposed, which aims to improve SVM by bootstrapping unlabeled data with self-training. The SVM classifier is iteratively refined through the augmentation of the training set. An improved self-training method is given by employing neighborhood graph for guarantying the reliability of newly added training examples. In detail, in each iteration of the self-training process, the local cut edge weight statistic is used to help estimate whether a newly labeled example is reliable or not, and only the reliable self-labeled examples are used to enlarge the labeled training set. Experiments show that, the improved self-training is beneficial and the proposed IS3VM algorithm can effectively exploit unlabeled data to achieve better performance, and is comparable to the-state-of-the-art semi-supervised SVM.
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Cheng, S., Huang, Q., Liu, J., Tang, X. (2013). A Novel Inductive Semi-supervised SVM with Graph-Based Self-training. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_11
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DOI: https://doi.org/10.1007/978-3-642-36669-7_11
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