An algorithm of estimating the generalization performance of RBF-SVM

Proceedings - 5th International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003

Volumn , Issue , 2003, Pages 61-66

  Dong, Chun Xi ^a   Yang, Shao Quan ^a   Xian, Rao ^a   Tang, Jian Long ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Computational intelligence; Equations; Error analysis; Function approximation; Kernel; Machine learning; Pattern recognition; Support vector machine classification; Support vector machines; Testing

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ERROR ANALYSIS; LEARNING SYSTEMS; PATTERN RECOGNITION; RADIAL BASIS FUNCTION NETWORKS; TESTING;

APPLICATION RANGE; COMPLEX COMPUTING; EQUATIONS; FUNCTION APPROXIMATION; GENERALIZATION PERFORMANCE; KERNEL; RADIAL BASIS FUNCTION(RBF); SUPPORT VECTOR MACHINE CLASSIFICATION;

SUPPORT VECTOR MACHINES;

EID: 33745645260     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCIMA.2003.1238101     Document Type: Conference Paper

References (9)

1. Vladimir N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc, 2000.
2. J.C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Kluwer Academic Publishers, Boston. 1999.
3. Devroye, L., Gyorfi, L., Lugosi, GA. A Probabilistic Theory of Pattern Recognition. Springer-Verlag New York, Inc, 1996.
4. Joachims, T. Estimating the Generalization Performance of a SVM Efficiently. LS VIII-Report 25, University Dortmund, Germany, 1999.
5. Lunts, A., Brailovskiy, V., Evaluation of Attributes Obtained in Statistical Decision Rules. Engineering Cybernetics, 3, P98-109, 1967.
6. Jaakkola, T., Haussler, D. Probabilistic Kernel Regression Models. Proceedings of the Seventh Workshop on AI and Statistics. San Francisco, 1999.
7. M. Opper, O. Winther. Gaussian Processes and SVM: Mean Field and Leave-one -out. Advances in Large Margin Classifiers, P311-326, Cambridge, MA, MIT Press, 2000.
8. V. Vapnik, O. Chapelle. Bounds on Error Expectation for Support Vector Machines. Neural. 12(9), 2000.
9. Murphy,-P.-M., Aha,-D.-W. Irvine, CA: University of California, Department of Information and Computer Science. [http://www.ics.uci.edu/~mlearn/MLRepository.html], 1994.