Fuzzy SVM Training Based on the Improved Particle Swarm Optimization

Authors:
Ying Li;Bendu Bai;Yanning Zhang
Affiliations:
School of Computer Science, Northwestern Polytechnical University, Xi'an, China 710072;School of Computer Science, Northwestern Polytechnical University, Xi'an, China 710072;School of Computer Science, Northwestern Polytechnical University, Xi'an, China 710072
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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The gregarious particle swarm optimizer (G-PSO)

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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.