TY - GEN
T1 - Fuzzy SVM training based on the improved particle swarm optimization
AU - Li, Ying
AU - Bai, Bendu
AU - Zhang, Yanning
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/53049096212
U2 - 10.1007/978-3-540-85984-0_68
DO - 10.1007/978-3-540-85984-0_68
M3 - 会议稿件
AN - SCOPUS:53049096212
SN - 3540859837
SN - 9783540859833
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 566
EP - 574
BT - Advanced Intelligent Computing Theories and Applications
T2 - 4th International Conference on Intelligent Computing, ICIC 2008
Y2 - 15 September 2008 through 18 September 2008
ER -