Evolutionary training of SVM for multiple category classification problems with self-adaptive parameters

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4140 LNAI, Issue , 2006, Pages 329-338

  Kuri Morales, Ángel ^a   Mejía Guevara, Iván ^b  

^a INSTITUTO TECNOLÓGICO AUTÓNOMO DE MÉXICO   (Mexico)

^b UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; PARAMETER ESTIMATION; PROBLEM SOLVING;

CLASSIFICATION PROBLEMS; EVOLUTIONARY TRAINING; SUPPORT VECTOR MACHINES (SVM);

LEARNING SYSTEMS;

EID: 33751376274     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11874850_37     Document Type: Conference Paper

References (26)

1. Jordaan, E. M. & Smits, G. F.: Estimation of the regularization parameter for support vector regression. Proc. of World Conference on Computational Intelligence I. Honolulu, Hawaii. (2002) 2785-2791.
2. Schmitdt, M., Grish, H.: Speaker identification via support vector classifiers. Proc. of International Conference on Acoustics, Speech and Signal Processing, (1996) 105-108.
3. Drucker, H., Wu, D., Vapnik, V.: Support vector machine for spam categorization. Trans. on Neural Networks, Vol. 10. IEEE, (1999) 1048-1054.
4. Vapnik, V., Golowich, S., Smola A.: Support vector method for function approximation, regression, estimation and signal processing. Adv. Neural Inform. Process. Syst., Vol. 9. (1996) 281-287.
5. Haykin, S.: Neural Networks. A comprehensive foundation. 2nd ed. Prentice Hall, New Jersey (1999).
6. Mercer, J.: Functions of positive and negative type, and their connection with the theory of integral equations. Transactions of the London Philosophical Society (A), Vol. 209. (1909) 415-446.
7. Shawe-Taylor, J., Cristianini, N,: Kernel Methods for Pattern Analysis. Cambridge University Press, UK (2004).
8. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, UK (2000).
9. Burges, C. J. C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, Vol. 2. (1998) 121-167.
10. Haykin, S., Data Mining and Knowledge Discovery 2, op. cit., pp. 318-350.
11. Haykin, S., Data Mining and Knowledge Discovery 2, op. cit., pp. 326-329.
12. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge, Massachusetts (1996).
13. Burges, C. J. C.: An Introduction to Genetic Algorithms, op. cit. 121-167.
14. Cristianini, N., An Introduction to Genetic Algorithms, op. cit., pp. 93-124.
15. Kuri, A., Gutiérrez, J.: Penalty Functions Methods for Constrained Optimisation with Genetic Algorithms. A Statistical Analysis. Lecture Notes in Artificial Intelligence, Vol. 2313. Springer-Verlag, (2002) 108-117.
16. Kuri, A., Gutiérrez, J., Lecture Notes in Artificial Intelligence, op. cit., pp.109-111.
17. Fung, G., Mangasarian, O. L.: Multicategory proximal support vector machine classifiers. Data Mining Institute Technical Report, (2001) 01-06.
18. Allwein, E. L., Shapire, R. E., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, Vol. 1, (2000) 113-141.
19. Bojanov, B., Hakopian, H., Sahakian, B.: Spline Functions and Multivariate Interpolations. Springer-Verlag, (1993).
20. Kuri, A., Mejía, I.: Evolutionary Training of SVM for Classification Problems with Self-Adaptive Parameters. Advances in Artificial Intelligence Theory, (Gelbukh, A., Monroy, R., Eds.), IPN, (2005), pp. 207-216.
21. Kuri Morales, A.: A Methodology for the Statistical Characterization of Genetic Algorithms. Proceedings of the Mexican International Congress on Artificial Intelligence, Springer-Verlag, (2002) 79-88.
22. Vapnik, V.: Estimation of Dependences Based on Empirical Data. Springer-Verlag, (1982).
23. Osuna, E., Freund, R., Girosi, F.: Improved training algorithm for support vector machines. Proc. IEEE Neural Networks in Signal Processing '97, (1997).
24. Mangasarian, O.L., Support Vector Machine Classification via Parameterless Robust Linear Programming, Data Mining Institute Technical Report 03-01, (2003).
25. Haykin, S., Data Mining Institute Technical Report, op. cit., 293-294.
26. Kuri Morales, A.: A Methodology for the Statistical Characterization of Genetic Algorithms, Lecture Notes in Artificial Intelligence: No. 2313, (Eds) Coello, C., Albornoz, A., Sucar, E., Cairó, O., Springer-Verlag, ISSN 0302-9743, ISNN 3-540-43475-5, (2002) 79-88.