Evolving kernel functions for SVMs by genetic programming

Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007

Volumn , Issue , 2007, Pages 19-24

  Diosan, Laura ^a,b   Rogozan, Alexandrina ^a   Pecuchet, Jean Pierre ^a  

^a University of Rouen   (France)

^b BABE BOLYAI UNIVERSITY   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FUNCTIONS; GENETIC ALGORITHMS; GENETIC PROGRAMMING; TREES (MATHEMATICS);

HYBRID TECHNIQUES; KERNEL FUNCTION; MATHEMATICAL EXPRESSIONS; NUMERICAL EXPERIMENTS; STANDARD KERNELS; SUPPORT VECTOR MACHINE ALGORITHM; SVM ALGORITHM; TREE ENCODING;

SUPPORT VECTOR MACHINES;

EID: 47349110721     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2007.47     Document Type: Conference Paper

References (14)

1. http://www.cs.ie.ntu.edu.tw/ cjlin/papers/guide/data/.
2. W. Banzhaf. Genetic programming: An introduction: On the automatic evolution of computer programs and its applications, 1998.
3. J. C. Burges. A tutorial on support vector machines for pattern recognition. In Knowledge Discovery and Data Mining, volume 2, pages 121-167. 1998.
4. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001.
5. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46(1/3):131, 2002.
6. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, 1995.
7. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press, 2000.
8. L. Diosan and M. Oltean. Evolving crossover operators for function optimization. In P. Collet and et al., editors, EuroGP, volume 3905 of LNCS, pages 97-108. Springer, 2006.
9. T. Howley and M. G. Madden. The genetic kernel support vector machine: Description and evaluation. Artif. Intell. Rev, 24(3-4):379-395, 2005.
10. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
11. G. R. G. Lanckriet and et al. Learning the kernel matrix with semidefinite programming. JMLR, 5:27-72, 2004.
12. B. Schoelkopf and A. J. Smola. Learning with Kernels. The MIT Press, 2002.
13. V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, 1995.
14. J. Weston and et al. Feature selection for SVMs. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS, pages 668-674. MIT Press, 2000.