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An overview on semi-supervised support vector machine

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Abstract

Support vector machine (SVM) is a machine learning method based on statistical learning theory. It has a lot of advantages, such as solid theoretical foundation, global optimization, the sparsity of the solution, nonlinear and generalization. The standard form of SVM only applies to supervised learning. Large amount of data generated in real life is unlabeled, and the standard form of SVM cannot make good use of these data to improve its learning ability. However, semi-supervised support vector machine (S3VM) is a good solution to this problem. This paper reviews the recent progress in semi-supervised support vector machine. First, the basic theory of S3VM is expounded and discussed in detail; then, the mainstream model of S3VM is presented, including transductive support vector machine, Laplacian support vector machine, S3VM training via the label mean, S3VM based on cluster kernel; finally, we give the conclusions and look ahead to the research on S3VM.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61379101).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding, Zhibin Zhu & Xiekai Zhang

  2. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding

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Correspondence to Shifei Ding.

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Ding, S., Zhu, Z. & Zhang, X. An overview on semi-supervised support vector machine. Neural Comput & Applic 28, 969–978 (2017). https://doi.org/10.1007/s00521-015-2113-7

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