Learning semi-supervised SVM with genetic algorithm

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn , Issue , 2007, Pages 1825-1830

  Adankon, Mathias M ^a   Cheriet, Mohamed ^a  

^a ÉCOLE DE TECHNOLOGIE SUPÉRIEURE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BOOLEAN FUNCTIONS; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER NETWORKS; DIESEL ENGINES; GENETIC ALGORITHMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; SUPERVISED LEARNING;

JOINT CONFERENCE; NEURAL NETWORKS; TRANSDUCTIVE SVM;

SUPPORT VECTOR MACHINES;

EID: 51749112463     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2007.4371235     Document Type: Conference Paper

References (19)

1. T. Mitchell, "The role of unlabeled data in supervised learning", Proceedings of the Sixth International Colloquium on Cognitive Science, San Sebastian, Spain, 1999.
2. Xiaojin Zhu, Semi-Supervised Learning Literature Survey, Computer Sciences, University of Wisconsin-Madison, Technical report no. 1530, 2005, http://www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf
3. M. Seeger, Learning with labeled and Unlabeled data , Technical report, University of Edinburgh, 2001.
4. B. Scholkopf , C. J. C. Burges and A. J. Smola, Advances in Kernel Methods: Support Vector Learning, The MIT Press, Cambridge, Massachusetts, 1999.
5. B. Scholkopf and A.J. Smola, Learning with Kernels, MIT Press, Cambridge, 2002.
6. V.N. Vapnik, Statistical learning theory, John Wiley and Sons, New York, 1998.
7. V. N. Vapnik, "Principles of Risk Minimization for Learning Theory". In Neural Information Processing Systems 4, Morgan Kaufman Publishers, San Mateo, CA, 1992.
8. O. Chapelle and A. Zien, "Semi-supervised classification by low density separation". In Proceeding of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.
9. T. Joachims, "Transductive Inference for Text Classification using Support Vector Machines". In Proceedings of ICML-99, 16th International Conference on Machine Learning, Morgan Kaufmann Publishers, San Francisco, US, pp. 200-209,1999.
10. K. Bennett and A. Demiriz, "Semi-Supervised Support Vector Machines". In Neural Information Processing Systems 11, pp. 368-374, 1998.
11. Holland, J. H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor, MI: University of Michigan Press, 1975.
12. Holland, J. H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. 2nd ed. Cambridge, MA: MIT Press, 1992.
13. Chapelle, O. and Vapnik, V. and Bousquet, O. and Mukherjee, S., "Choosing multiple parameters for support vector machines". In Machine Learning, 2001.
14. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Massachusetts, 1989.
15. W. M. Spears, The role of Mutation and Recombinaison in Evolutionary Algorithms , Ph.D. Thesis, Fairfax, VA: George Mason University, 1998.
16. Adankon M. M., Cheriet M. and Ayat N. E., "Optimizing Resources in Model Selection for Support Vector Machines", IEEE International Joint Conference in Neural Networks , Vol. 2, pp. 925-930, Montreal, 2005.
17. Piero P. Bonissone, Raj Subbu, Neil Eklund and Thomas R. Kiehl, "Evolutionary algorithms + domain knowledge = real-world evolutionary computation". In IEEE Trans. Evolutionary Computation, Vol. 10(3), pp. 256-280, 2006.
18. Y. Grandvalet and Y. Bengio, "Semi-supervised Learning by Entropy Minimization". In Neural Information Processing Systems 17, MIT Press, Cambridge, MA, pp.529-536, 2004.
19. M. Szummer and T. Jaakkola, "Partially labeled classification with Markov random walks". In Neural Information Processing Systems, volume 14, 2001.