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Proximal parametric-margin support vector classifier and its applications

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Abstract

As a development of powerful SVMs, the recently proposed parametric-margin ν-support vector machine (par-ν-SVM) is good at dealing with heteroscedastic noise classification problems. In this paper, we propose a novel and fast proximal parametric-margin support vector classifier (PPSVC), based on the par-ν-SVM. In the PPSVC, we maximize a novel proximal parametric-margin by solving a small system of linear equations, while the par-ν-SVM maximizes the parametric-margin by solving a quadratic programming problem. Therefore, our PPSVC not only is useful with the case of heteroscedastic noise but also has a much faster learning speed compared with the par-ν-SVM. Experimental results on several artificial and public available datasets show the advantages of our PPSVC both on the generalization ability and learning speed. Furthermore, we investigate the performance of the proposed PPSVC on the text categorization problem. The experimental results on two benchmark text corpora show the practicability and effectiveness of the proposed PPSVC.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No.60973155, No.11201426, No.10971223 and No.11071252), the Project 20121053 supported by Graduate Innovation Fund of Jilin University and the Zhejiang Provincial Natural Science Foundation of China (No.LQ12A01020).

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Authors and Affiliations

  1. Mathematics College, Jilin University, Changchun, People’s Republic of China

    Zhen Wang & Tie-Ru Wu

  2. Zhijiang College, Zhejiang University of Technology, Hangzhou, People’s Republic of China

    Yuan-Hai Shao

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  1. Zhen Wang
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  2. Yuan-Hai Shao
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  3. Tie-Ru Wu
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Correspondence to Zhen Wang.

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Wang, Z., Shao, YH. & Wu, TR. Proximal parametric-margin support vector classifier and its applications. Neural Comput & Applic 24, 755–764 (2014). https://doi.org/10.1007/s00521-012-1278-6

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