Abstract
The recently proposed twin parametric insensitive support vector regression, denoted by TPISVR, which solves two dual quadratic programming problems (QPPs). However, TPISVR has at least four regularization parameters that need regulating. In this paper, we increase the efficiency of TPISVR from two aspects. Fist, we propose a novel least squares twin parametric insensitive support vector regression, called LSTPISVR for short. Compared with the traditional solution method, LSTPISVR can improve the training speed without loss of generalization. Second, a discrete binary particle swarm optimization (BPSO) algorithm is introduced to do the parameter selection. Computational results on several synthetic as well as benchmark datasets confirm the great improvements on the training process of our LSTPISVR.
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References
Peng, X.J.: TSVR: an efficient twin support vector machine for regression. Neural Netw. 23, 365–372 (2010)
Shao, P.Y.: New support vector algorithms with parametric insensitive/margin model. Neural Netw. 23(1), 60–73 (2010)
Peng, X.J.: Efficient twin parametric insensitive support vector regression model. Neurocomputing 79, 26–38 (2012)
Wong, T.C., Ngan, S.C.: A comparison of hybrid genetic algorithm and hybrid particle swarm optimization to minimize make-span for assembly job shop. Appl. Soft Comput. 13(3), 1391–1399 (2013)
Jin, W., Zhang, J.Q., Zhang, X.A.: Face recognition method based on support vector machine and particle swarm optimization. Expert Syst. Appl. 38(4), 4390–4393 (2011)
Acknowledgments
This work is supported by the National Natural Science Foundation under Grant no. 61662005, the Science and Technology Research Project of Guangxi University under Grant no. KY2015YB076, the Talent Research Projects of Guangxi University for Nationalities under Grant no. 2014MDQD018 and the open fund of Key Laboratory of Guangxi High Schools for Complex System & Computational Intelligence (No. 15CI01D).
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Wei, X., Huang, H. (2017). BPSO Optimizing for Least Squares Twin Parametric Insensitive Support Vector Regression. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_45
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DOI: https://doi.org/10.1007/978-3-319-63315-2_45
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