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Support vector set selection using pulse-coupled neural networks

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Abstract

A candidate set of support vectors is selected by using pulse-coupled neural networks to reduce computational cost in learning phase for support vector machines (SVMs). The size of the candidate set of support vectors selected this way is smaller than that of the original training samples so that the computation complexity in learning process for support vectors machines based on this candidate set is reduced and the learning process is accelerated. On the other hand, the candidate set of support vectors includes almost all support vectors, and the performance of the SVM based on this candidate set matches the performance when the full training samples are used.

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Acknowledgments

This work was supported by Specialized Research Fund for the Doctoral Program of Higher Education under Grant 2010081110053 and National Program on Key Basic Research Project (973 Program) under Grant 2011CB302201, partially supported by National Science Foundation of China under Grant 61375065.

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Authors and Affiliations

  1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Yunxia Li

  2. Machine Intelligence Lab, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of China

    Zhang Yi & Jian Cheng Lv

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  1. Yunxia Li
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  2. Zhang Yi
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  3. Jian Cheng Lv
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Correspondence to Jian Cheng Lv.

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Li, Y., Yi, Z. & Lv, J.C. Support vector set selection using pulse-coupled neural networks. Neural Comput & Applic 25, 401–410 (2014). https://doi.org/10.1007/s00521-013-1506-8

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