TY - GEN
T1 - An improved particle swarm optimization for SVM training
AU - Li, Ying
AU - Tong, Yan
AU - Bai, Bendu
AU - Zhang, Yanning
PY - 2007
Y1 - 2007
N2 - Since training a SVM requires solving a constrained quadratic programming problem which becomes difficult for very large dataseis, an improved particle swarm optimization algorithm is proposed as an alternative to current numeric SVM training methods. 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 show that the improved algorithm is feasible and effective for SVM training.
AB - Since training a SVM requires solving a constrained quadratic programming problem which becomes difficult for very large dataseis, an improved particle swarm optimization algorithm is proposed as an alternative to current numeric SVM training methods. 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 show that the improved algorithm is feasible and effective for SVM training.
UR - http://www.scopus.com/inward/record.url?scp=38049089917&partnerID=8YFLogxK
U2 - 10.1109/ICNC.2007.222
DO - 10.1109/ICNC.2007.222
M3 - 会议稿件
AN - SCOPUS:38049089917
SN - 0769528759
SN - 9780769528755
T3 - Proceedings - Third International Conference on Natural Computation, ICNC 2007
SP - 611
EP - 615
BT - Proceedings - Third International Conference on Natural Computation, ICNC 2007
T2 - 3rd International Conference on Natural Computation, ICNC 2007
Y2 - 24 August 2007 through 27 August 2007
ER -