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Soft Quadratic Surface Support Vector Machine for Binary Classification

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

Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC 27695-7906, USA

E-mail Address: fang@ncsu.edu

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Zhibin Deng

School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China

E-mail Address: zhibindeng@ucas.ac.cn

Corresponding author.

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, and

Xiaoling Guo

School of Science, China University of Mining and Technology, Beijing 100083, P. R. China

E-mail Address: guoxiaoling@ucas.ac.cn

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https://doi.org/10.1142/S0217595916500469Cited by:34 (Source: Crossref)

Abstract

In this paper, a kernel-free soft quadratic surface support vector machine model is proposed for binary classification directly using a quadratic function for separation. Properties (including the solvability, uniqueness and support vector representation of the optimal solution) of the proposed model are derived. Results of computational experiments on some artificial and real-world classifying data sets indicate that the proposed soft quadratic surface support vector machine model may outperform Dagher’s quadratic model and other soft support vector machine models with a Quadratic or Gaussian kernel in terms of the classification accuracy and robustness.

Keywords:

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