Abstract
In this paper, an SVM classification system based on particle swarm optimization (PSO) is proposed to improve the generalization performance of the SVM classifier. Authors have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function. The experiments are conducted on the basis of benchmark dataset. Fourteen obtained results clearly confirm the superiority of the PSO-SVM approach.
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Li, J., Li, B. (2014). Parameters Selection for Support Vector Machine Based on Particle Swarm Optimization. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_5
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DOI: https://doi.org/10.1007/978-3-319-09333-8_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09332-1
Online ISBN: 978-3-319-09333-8
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