Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space

Pattern Recognition

Volumn 42, Issue 5, 2009, Pages 710-717

  Wu, Kuo Ping ^a   Wang, Sheng De ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Inter cluster distances; Kernel parameters; Support vector machines; SVM

Indexed keywords

CLASSIFIERS; IMAGE RETRIEVAL; LEARNING SYSTEMS; VECTORS;

COMPUTATION TIMES; FEATURE SPACES; GRID SEARCHES; INTER-CLUSTER DISTANCES; KERNEL PARAMETERS; PENALTY PARAMETERS; SVM; SVM CLASSIFIERS; TESTING ACCURACIES; TESTING PROCESSES; TRAINING PROCESSES; TRAINING TIMES;

SUPPORT VECTOR MACHINES;

EID: 58249083168     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.08.030     Document Type: Article

References (29)

1. Vapnik V. Statistical Learning Theory (1998), Wiley, New York
2. Hsu C.-W., and Lin C.-J. A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 13 2 (2002) 415-425
3. Ancona N., Cicirelli G., Stella E., and Distante A. Object detection in images: run-time complexity and parameter selection of support vector machines. 16th International Conference on Pattern Recognition vol. 2 (11-15 August, 2002) 426-429
4. Rojas S.A., and Fernandez-Reyes D. Adapting multiple kernel parameters for support vector machines using genetic algorithms. The 2005 IEEE Congress on Evolutionary Computation vol. 1 (02-05 September, 2005) 626-631
5. Liang X., and Liu F. Choosing multiple parameters for SVM based on genetic algorithm. 6th International Conference on Signal Processing vol. 1 (26-30 August, 2002) 117-119
6. Liu H.-J., Wang Y.-N., and Lu X.-F. A method to choose kernel function and its parameters for support vector machines. Proceedings of 2005 International Conference on Machine Learning and Cybernetics vol. 7 (18-21 August, 2005) 4277-4280
7. Liu S., Jia C.-Y., and Ma H. A new weighted support vector machine with GA-based parameter selection. Proceedings of 2005 International Conference on Machine Learning and Cybernetics vol. 7 (18-21 August, 2005) 4351-4355
8. Quang A.T., Zhang Q.-L., and Li X. Evolving support vector machine parameters. Proceedings of 2002 International Conference on Machine Learning and Cybernetics vol. 1 (4-5 November, 2002) 548-551
9. Imbault F., and Lebart K. A stochastic optimization approach for parameter tuning of support vector machines. Proceedings of 17th International Conference on Pattern Recognition vol. 4 (23-26 August, 2004) 597-600
10. J.C. Platt, Sequential minimal optimization: a fast algorithm for training support vector machines, Microsoft Research, Technical Report MST-TR-98-14, 1998.
11. Maruyama K.-I., Maruyama M., Miyao H., and Nakano Y. A method to make multiple hypotheses with high cumulative recognition rate using SVMs. Pattern Recognition 37 (2004) 241-251
12. Hush D., and Scovel C. Polynomial-time decomposition algorithms for support vector machines. Mach. Learn. 51 (2003) 51V71
13. Debnath R., and Takahashi H. An efficient method for tuning kernel parameter of the support vector machine. IEEE International Symposium on Communications and Information Technology vol. 2 (26-29 October, 2004) 1023-1028
14. Bi L.-P., Huang H., Zheng Z.-Y., and Song H.-T. New heuristic for determination Gaussian kernel's parameter. Proceedings of 2005 International Conference on Machine Learning and Cybernetics vol. 7 (18-21 August, 2005) 4299-4304
15. Keerthi S.S., and Lin C.-J. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput. 15 7 (2003) 1667-1689
16. H.-T. Lin, C.-J. Lin, A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University.
17. C.-W. Hsu, C.-C. Chang, C.-J. Lin, A practical guide to support vector classification. [Online] Available from World Wide Web: 〈http://www.csie.ntu.edu.tw/∼cjlin/libsvm〉.
18. Bottou L., Cortes C., Denker J., Drucker H., Guyon I., Jackel L., LeCun Y., Muller U., Sackinger E., Simard P., and Vapnik V. Comparison of classifier methods: a case study in handwritten digit recognition. Proceedings of International Conference on Pattern Recognition (1994) 77-87
19. Kreßel U. Pairwise classification and support vector machines. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds). Advances in Kernel Methods-Support Vector Learning (1999), MIT Press, Cambridge, MA
20. Platt J.C., Cristianini N., and Show-Tayler J. Large margin DAG's for multiclass classification. Advances in Neural Information Processing Systems vol. 12 (2000), MIT Press, Cambridge, MA 547-553
21. F. Takahashi, S. Abe, Optimizing directed acyclic graph support vector machines, in: Proceedings of the Artificial Neural Networks in Pattern Recognition (ANNPR 2003), September 2003, pp. 166-170.
22. T. Phetkaew, B. Kijsirikul, W. Rivepiboon, Reordering adaptive directed acyclic graphs: an improved algorithm for multiclass support vector machines, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2003), vol. 2, 2003, pp. 1605-1610.
23. K.-P. Wu, S.-D. Wang, Choosing the kernel parameters of support vector machines according to the inter-cluster distance, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2006), 2006.
24. Bezdek C., and Pal N.R. Some new indexes of cluster validity. IEEE Trans. Syst. Man Cybern. Part B Cybern. 28 3 (1998) 301-315
25. Platt J.C. Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds). Advances in Kernel Methods-Support Vector Learning (1999), MIT Press, Cambridge, MA
26. D. Michie, D.J. Spiegelhalter, C.C. Taylor, Machine learning, neural and statistical classification, 1994. [Online] Available: 〈ftp.ncc.up.pt/pub/statlog/〉.
27. C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines. [Online] Available from World Wide Web: 〈http://www.csie.ntu.edu.tw/∼cjlin/libsvm〉.
28. Dong J.X., Krzyzak A., and Suen C.Y. Fast SVM training algorithm with decomposition on very large datasets. IEEE Trans. Pattern Anal. Mach. Intell. 27 4 (2005) 603-618
29. R. Collobert, S. Bengio, J. Mariethoz, Torch. [Online] Available from World Wide Web: 〈http://www.torch.ch/〉.