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Improvement on projection twin support vector machine

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Abstract

Traditional projection twin support vector machines (SVMs) ignore the differences between the categories when establishing the objective functions, which would lessen their generalization performance. To solve the issue, an improved projection twin SVM (abbreviated as IPTSVM) is proposed in this paper, which aims to find two projected directions via a single quadratic programming problem. In their respective subspace, the projected sample points belonging to each category are far from those of the other class. Meanwhile, to enhance the performance, the recursive arithmetic seeks for more than one projection directions for each class. Besides, an effective clipping dual coordinate descent model is adopted to solve the dual problem to accelerate the training process. The linear IPTSVM model could be changed into the nonlinear model by using the kernel metric. Furthermore, the multi-label version of IPTSVM model is developed to deal with the multi-label learning problems. Experiments on a set of public datasets show that the IPTSVM model has significant advantages over the other models in terms of generalization performance.

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Acknowledgements

This work is supported by ‘the Fundamental Research Funds for the Central Universities’ (2016B02914), National Natural Science Foundation (NNSF) of China (61403122) and Science and Techonology Development Funds of Jiangsu Province (2015030-03).

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  1. College of Mechanical and Electrical Engineering, Hohai University, Changzhou, 213022, Jiangsu, China

    Xiaomin Xie

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Correspondence to Xiaomin Xie.

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We declare that we have not any financial and personal relationships with other people or organizations that can inappropriately influence this work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled ‘Improvement on projection twin support vector machine.’

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Xie, X. Improvement on projection twin support vector machine. Neural Comput & Applic 30, 371–387 (2018). https://doi.org/10.1007/s00521-017-3237-8

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