Parameters optimization of support vector regression based on immune particle swarm optimization algorithm

2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09

Volumn , Issue , 2009, Pages 997-1000

  Wang, Yan ^a   Wang, Juexin ^a   Du, Wei ^a   Zhang, Chen ^a   Zhang, Yu ^a   Zhou, Chunguang ^a  

^a JILIN UNIVERSITY   (China)

Author keywords

Immune algorithm; Parameters optimization; Particle swarm optimization; Support vector regression

Indexed keywords

CROSS VALIDATION; DATA SETS; GENERALIZATION PERFORMANCE; IMMUNE ALGORITHM; IMMUNE ALGORITHMS; IMMUNE PARTICLE SWARM OPTIMIZATION; LEARNING ACCURACY; LEAVE-ONE-OUT; MUTATION PROCESS; PARAMETERS OPTIMIZATION; REGRESSION MODEL; SIMULATION RESULT; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS;

ALGORITHMS; CALCULATIONS; REGRESSION ANALYSIS; SEMICONDUCTING GERMANIUM COMPOUNDS; VECTORS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 67650677303     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1543834.1543992     Document Type: Conference Paper

References (12)

1. Vapnik, V. 1995, The Nature of Statistical Learning Theory, New York: Spring-Verlag Press.
2. Vapnik, V. 1999, An overview of Statistical Learning Theory. IEEE Trans. On Neural Networks, 10(5):988-999,
3. Bennett, K. and Campbell, C. 2000, Support vector machine: hype on hallelujah, SIGKDD Exploration, 2(2):1-13
4. Smola,A.J. and Scholkopf,B. 2004, A tutorial on support vector regression, Statistics and Computing, 14:199-222
5. Kennedy J. and Eberhart, R. C. 1995, "Particle Swarm Optimization," in Proc. IEEE Conf. Neural Networks IV, Vol. 4, pp. 1942-1948.
6. Shi, Y., and Eberhart, R. C., 1998, "A Modified Particle Swarm Optimizer," Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69-73.
7. R.C. Eberhart, and Shi, Y. 2001, Particle Swarm Optimization: Developments, Applications and Resources, in: Proceedings of International Conference on Evolutionary Computation, pp. 81-86.
8. Hunt, J. E. and Cooke, D. E. 1996, "Learning using an artificial immune system", Journal of Network and Computer Applications, vol. 19, pp. 189-212.
9. Cherkassky,V. and Ma,Y. 2004, Practical selection of SVM parameters and noise,estimation for SVM regression [J ]. Neural Networks, 17 (1) :113- 126.
10. Xin Wang, Chunhua Yang, Bin Qin, Weihua Gui. 2005, Parameter selection of support vector regression based on hybrid optimization algorithm and its application. Journal of Control Theory and Applications, 2005, 4 : 371-376
11. Kalivas, John H., 1997, "Two Data Sets of Near Infrared Spectra," Chemometrics and Intelligent Laboratory Systems, v.37 (1997) pp.255-259
12. Haenlein, Michael and Andreas M. Kaplan , 2004, "A Beginner's Guide to Partial Least Squares Analysis, Understanding Statistics, 3(4), 283-297".