Accès libre

Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

 et    | 05 oct. 2015
À propos de cet article

Citez

1. Vapnik, V. N. The Nature of Statistical Learning Theory. NY, Springer-Verlag, 1995.10.1007/978-1-4757-2440-0Search in Google Scholar

2. Vapnik, V. N. Estimation of Dependencies Based on Empirical Data. Berlin, Springer-Verlag, 1982.Search in Google Scholar

3. Guo, Yaxiang, Shifei Ding. Advances in Support Vector Machines. – Computer Science, Vol. 38, 2011, No 2, pp. 14-17.Search in Google Scholar

4. Wu, Q., R. Law, E. Wu. A Hybrid-Forecasting Model Reducing Gaussian Noise Based on the Gaussian Support Vector Regression Machine and Chaotic Particle Swarm Optimization. – Information Sciences, Vol. 23, 2013, No 8, pp. 96-110.10.1016/j.ins.2013.02.017Search in Google Scholar

5. Ding, G., L. Wang, P. Yang et al. Diagnosis Model Based on Least Squares Support Vector Machine Optimized by Multi-Swarm Cooperative Chaos Particle Swarm Optimization and its Application. – Journal of Computers, Vol. 8, 2013, No 4, pp. 975-982.10.4304/jcp.8.4.975-982Search in Google Scholar

6. Wang, Haiyan, Jianhui Li, Fenglei Yang. Review of Theory and Support Vector Machine Algorithm. – Application of Computer, Vol. 31, 2014, No 5, pp. 1281-1286.Search in Google Scholar

7. Che, J. X. Support Vector Regression Based on Optimal Training Subset and Adaptive Particle Swarm Optimization Algorithm. – Applied Soft Computing, Vol. 13, 2013, No 8, pp. 3473-3481.10.1016/j.asoc.2013.04.003Search in Google Scholar

8. Zhang, L., J. Wang. Optimizing Parameters of Support Vector Machines Using Team-Search-Based Particle Swarm Optimization. – Engineering Computations, Vol. 32, 2015, No 5, pp. 119-127.10.1108/EC-12-2013-0310Search in Google Scholar

9. Liu, Xiang dong, Luobin, Zhaoquan Chen. Support Vector Machine Optimal Model Selection Research. – Computer Research and Development, Vol. 42, 2015, No 4, pp. 576-581.10.1360/crad20050407Search in Google Scholar

10. Vapnik, V., E. Levin, Y. Le Cun. A Measuring the VC-Dimension of a Learning Machine. – Neural Computation, Vol. 6, 1994, pp. 851-876.10.1162/neco.1994.6.5.851Search in Google Scholar

11. Kuang, Fangjun, Weihong Xu, Shiyang Zhang. A Optimization and Application of Improved Chaos Particle Swarm Mixed Kernel SVM Based on Parameters. – Application of Computer, Vol. 31, 2014, No 3, pp. 671-674.Search in Google Scholar

12. Kennedy, J., R. L. Eberhart. Particle Swarm Optimization. – In: Proc. of IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.Search in Google Scholar

13. Chen, Haiying, Minghui Wu. Prediction of Chaotic Time Series Prediction Model Uniform Design Parameters. – Computer Science, Vol. 38, 2011, No 2, pp. 14-17.Search in Google Scholar

14. Tang, qi, Hongrui Wang, Xinyi Xu. Hybrid Kernels SVM Hydrologic Time Series Model and Its Application. – Systems Engineering Theory and Practice, Vol. 34, 2014, No 2, pp. 521-529.Search in Google Scholar

15. You, M, T. Jiang. New Method for Target Identification in a Foliage Environment Using Selected Bispectra and Chaos Particle Swarm Optimisation-Based Support Vector Machine. – Signal Processing, Vol. 8, 2014, No 1, pp. 76-84.10.1049/iet-spr.2012.0389Search in Google Scholar

16. Xiong, Nan, Baifen Liu. LSSVM Optimization Based on Adaptive Particle Swarm Network Traffic Online Prediction. – LSSVM Optimization Based on Adaptive Particle Swarm Network Traffic Online Prediction.Search in Google Scholar

17. Lu, Lu, Lianglun Cheng. Improved Network Traffic Forecast Model Cuckoo Search Algorithm Optimization of SVM. – Computer Applications and Software, Vol. 32, 2015, No 1, pp. 124-127.Search in Google Scholar

18. Chen, F., B. Tang, T. Song et al. Multi-Fault Diagnosis Study on Roller Bearing Based on Multi-Kernel Support Vector Machine with Chaotic Particle Swarm Optimization. – Measurement, Vol. 47, 2014, No 9, pp. 576-590.10.1016/j.measurement.2013.08.021Search in Google Scholar

eISSN:
1314-4081
Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Computer Sciences, Information Technology