GP-based kernel evolution for L2-Regularization Networks

Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Volumn , Issue , 2014, Pages 1674-1681

  Scardapane, Simone ^a   Comminiello, Danilo ^a   Scarpiniti, Michele ^a   Uncini, Aurelio ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; GENETIC ALGORITHMS; GENETIC PROGRAMMING; LEARNING SYSTEMS; SUPPORT VECTOR MACHINES;

ADAPTIVE CRITERION; DESIGN PARAMETERS; DIVERSITY MAINTENANCE; KERNEL-BASED LEARNING; PRIORI INFORMATION; REGULARIZATION NETWORKS; STANDARD KERNELS; SUPPORT VECTOR MACHINE (SVMS);

LEARNING ALGORITHMS;

EID: 84908577889     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2014.6900389     Document Type: Conference Paper

References (14)

1. A. Aizerman, E. M. Braverman, and L. I. Rozoner, "Theoretical foundations of the potential function method in pattern recognition learning," Automation and remote control, vol. 25, pp. 821-837, 1964.
2. B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proceedings of the fifth annual workshop on Computational learning theory. ACM, 1992, pp. 144-152.
3. T. Hofmann, B. Schö lkopf, and A. J. Smola, "Kernel methods in machine learning," The Annals of Statistics, vol. 36, no. 3, pp. 1171-1220, Jun. 2008.
4. C. A. Micchelli, Y. Xu, and H. Zhang, "Universal kernels," The Journal of Machine Learning Research, vol. 7, p. 26512667, 2006.
5. X. Xu, I. W. Tsang, and D. Xu, "Soft margin multiple kernel learning," IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 5, p. 749761, May 2013.
6. R. Poli, W. Langdon, N. McPhee, and J. Koza, A field guide to genetic programming, 2008.
7. T. Howley and M. G. Madden, "The Genetic Kernel Support Vector Machine: Description and Evaluation," Artificial Intelligence Review, vol. 24, no. 3-4, pp. 379-395, Nov. 2005. [Online]. Available: Http://www.springerlink.com/index/10.1007/s10462-005-9009-3
8. K. M. Sullivan and S. Luke, "Evolving kernels for support vector machine classification," in Proceedings of the 9th annual conference on Genetic and evolutionary computation-GECCO '07. ACM Press, 2007, p. 1702.
9. L. Dioan, A. Rogozan, and J.-P. Pecuchet, "Improving classification performance of Support Vector Machine by genetically optimising kernel shape and hyper-parameters," Applied Intelligence, vol. 36, no. 2, pp. 280-294, Oct. 2010.
10. P. Koch, B. Bischl, O. Flasch, T. Bartz-Beielstein, C. Weihs, and W. Konen, "Tuning and evolution of support vector kernels," Evolutionary Intelligence, vol. 5, no. 3, pp. 153-170, May 2012.
11. T. Evgeniou, M. Pontil, and T. Poggio, "Regularization networks and support vector machines," Advances in Computational Mathematics, vol. 13, pp. 1-50, 2000.
12. A. Ekrt and S. Z. Nmeth, "Maintaining the diversity of genetic programs," in Genetic Programming, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002, vol. 2278, pp. 162-171.
13. R. A. Horn and C. R. Johnson, Matrix analysis. Cambridge university press, 2012.
14. E. K. Burke, S. Gustafson, and G. Kendall, "Diversity in genetic programming: An analysis of measures and correlation with fitness," IEEE Transactions on Evolutionary Computation, vol. 8, no. 1, pp. 47-62, 2004.