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A new branch-and-bound approach to semi-supervised support vector machine

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Abstract

This paper develops a branch-and-bound algorithm to solve the 2-norm soft margin semi-supervised support vector machine. First, the original problem is reformulated as a non-convex quadratically constrained quadratic programming problem with a simple structure. Then, we propose a new lower bound estimator which is conceptually simple and easy to be implemented in the branch-and-bound scheme. Since this estimator preserves both a high efficiency and a relatively good quality in the convex relaxation, it leads to a high total efficiency in the whole computational process. The numerical tests on both artificial and real-world data sets demonstrate the better effectiveness and efficiency of this proposed approach, which is compared to other well-known methods on different semi-supervised support vector machine models.

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Acknowledgments

Tian’s research has been supported by the National Natural Science Foundation of China Grants \(\#\) 11401485 and \(\#\) 71331004.

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Authors and Affiliations

  1. School of Business Administration and Research Center for Big Data, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Ye Tian

  2. School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, 116025, China

    Jian Luo

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  1. Ye Tian
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  2. Jian Luo
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Correspondence to Jian Luo.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Tian, Y., Luo, J. A new branch-and-bound approach to semi-supervised support vector machine. Soft Comput 21, 245–254 (2017). https://doi.org/10.1007/s00500-016-2089-y

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