Evolutionary learning with kernels: A generic solution for large margin problems

GECCO 2006 - Genetic and Evolutionary Computation Conference

Volumn 2, Issue , 2006, Pages 1553-1560

  Mierswa, Ingo ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Evolution strategies; Kernel methods; Machine learning; Particle swarms; Support vector machines

Indexed keywords

EVOLUTION STRATEGIES; KERNEL METHODS; PARTICLE SWARMS; SUPPORT VECTOR MACHINES;

COMPUTATION THEORY; EVOLUTIONARY ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING; QUADRATIC PROGRAMMING; VECTORS;

LEARNING SYSTEMS;

EID: 33750233846     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. H.-G. Beyer and H.-P. Schwefel. Evolution strategies: A comprehensive introduction. Journal Natural Computing, 1(1):2-52, 2002.
2. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167, 1998.
3. G. Camps-Valls, J. Martin-Guerrero, J. Rojo-Alvarez, and E. Soria-Olivas. Fuzzy sigmoid kernel for support vector classifiers. Neurocomputing, 62:501-506, 2004.
4. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001.
5. S. Fischer, R. Klinkenberg, I. Mierswa, and O. Ritthoff. Yale: Yet Another Learning Environment - Tutorial. Technical Report CI-136/02, Collaborative Research Center 531, University of Dortmund, Dortmund, Germany, 2002.
6. F. Friedrichs and C. Igel. Evolutionary tuning of multiple svm parameters. In Proc. of the 12th European Symposium on Artificial Neural Networks (ESANN 2004), pages 519-524, 2004.
7. H. Frp̈hlich, O. Chapelle, and B. Schölkopf. Feature selection for support vector machines using genetic algorithms. International Journal on Artificial Intelligence Tools, 13(4):791-800, 2004.
8. B. Haasdonk. Feature space interpretation of svms with indefinite kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4):482-492, 2005.
9. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, 2001.
10. T. Howley and M. Madden. The genetic kernel support vector machine: Description and evaluation. Artificial Intelligence Review, 2005.
11. W. James and C. Stein. Estimation with quadratic loss. In Proceedings of the Fourth Berkeley Symposium on Mathematics, Statistics and Probability, pages 361-380, 1960.
12. T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, chapter 11. MIT Press, Cambridge, MA, 1999.
13. T. Joachims. A support vector method for multivariate performance measures. In Proc. of the International Conference on Machine Learning (ICML), pages 377-384, 2005.
14. J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proc. of the International Conference on Neural Networks, pages 1942-1948, 1995.
15. H.-T. Lin and C.-J. Lin. A study on sigmoid kernels for svm and the training of non-psd kernels by smo-type methods, March 2003.
16. D. Newman, S. Hettich, C. Blake, and C. Merz. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
17. C. Ong, X. Mary, S. Canu, and A. J. Smola. Learning with non-positive kernels. In Proc. of the 21st International Conference on Machine Learning (ICML), pages 639-646, 2004.
18. I. Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, 1973.
19. T. Runarsson and S. Sigurdsson. Asynchronous parallel evolutionary model selection for support vector machines. Neural Information Processing, 3(3):59-67, 2004.
20. S. Rüping. mySVM Manual. Universität Dortmund, Lehrstuhl Informatik VIII, 2000. http://www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVM/.
21. B. Schölkopf and A. J. Smola. Learning with Kernels -Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.
22. A. Smola, B. Schölkopf, and K.-R. Müller. General cost functions for support vector regression. In Proceedings of the 8th International Conference on Artificial Neural Networks, pages 79-83, 1998.
23. A. J. Smola, Z. L. Ovari, and R. C. Williamson. Regularization with dot-product kernels. In Proc. of the Neural Information Processing Systems (NIPS), pages 308-314, 2000.
24. Statlib - datasets archive. http://lib.stat.cmu.edu/datasets/.
25. T. Storch. On the impact of objective function transformations on evolutionary and black-box algorithms. In Proc. of the Genetic and Evolutionary Computation Conference (GECCO), pages 833-840, 2005.
26. B. Taskar, V. Chatalbashev, D. Koller, and C. Guestrin. Learning structured prediction models: A large margin approach. In Proc. of the International Conference on Machine Learning (ICML), 2005.
27. V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
28. V. Vapnik and A. Chervonenkis. The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognition and Image Analysis, 1(3):283-305, 1991.