Abstract
In this paper, an improved particle swarm optimization algorithm is proposed to train the fuzzy support vector machine (FSVM) for pattern multi-classification. In the improved algorithm, the particles studies not only from itself and the best one but also from the mean value of some other particles. In addition, adaptive mutation was introduced to reduce the rate of premature convergence. The experimental results on MNIST character recognition show that the improved algorithm is feasible and effective for FSVM training.
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Li, Y., Bai, B., Zhang, Y. (2008). Fuzzy SVM Training Based on the Improved Particle Swarm Optimization. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_68
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DOI: https://doi.org/10.1007/978-3-540-85984-0_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
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