A bayesian framework for regularized SVM parameter estimation

Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Volumn , Issue , 2004, Pages 99-105

  Gregor, Jens ^a   Liu, Zhenqiu ^a  

^a University of Tennessee Knoxville   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN FRAMEWORKS; LINEAR CLASSIFIERS; MAPPING FUNCTIONS; SUPPORT VECTOR MACHINES (SMV);

ALGORITHMS; CLASSIFIERS; DATA MINING; NUMERICAL METHODS; OPTIMIZATION; PARAMETER ESTIMATION; VECTORS;

PATTERN RECOGNITION;

EID: 19544392137     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2004.10094     Document Type: Conference Paper

References (15)

1. C. Blake and C. Merz. UCI Repository of machine learning databases. Technical Report http://www.ics.uci.edu/~mlearn/MLRepository.html, Department of Information and Computer Sciences, University of California, Irvine, 1998.
2. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46:131-159, 2002.
3. K.-M. Chung, W.-C. Kao, C.-L. Sun, L.-L. Wang, and C.-J. Lin. Radius margin bounds for support vector machines with the RBF kernel. Neural Computation, 15:2643-2681, 2003.
4. C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:273-297, 1995.
5. G. Fung and O. Mangasarian. A feature selection Newton method for support vector machine classification. Technical Report DMI-02-03, Data Mining Institute, University of Wisconsin, 2002.
6. T. Graepel, R. Herbrich, and K. Obermayer. Bayesian transduction. Advances in Neural Information System Processing, 12:456-462, 2000.
7. S. Gunn. Support vector machines for classification and regression. Technical Report ISIS-1-98, Image, Speech and Intelligent Systems Research Group, University of Southampton, 1998.
8. S. Keerthi. Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Trans. Neural Networks, 13:1225-1229, 2002.
9. J. Kwok. Integrating the evidence framework and the support vector machine. Proc. European Symp. Artificial Neural Networks, ESANN'99 (Bruges, Belgium), pages 177-182, 1999.
10. Y. Lin. Support vector machines and the Bayes rule in classification. Data Mining and Knowledge Discovery, 6:259-275, 2002.
11. D. MacKay. Bayesian interpolation. Neural Computation, 4:415-447, 1992.
12. I. Nabney. Netlab: Algorithms for Pattern Recognition. Springer, 2001.
13. P. Sollich. Probabilistic interpretation and Bayesian methods for support vector machines. Proc. Intl. Conf. Artificial Neural Networks (ICANN'99), pages 177-182, 1999.
14. T. van Gestel, J. Suykens, G. Lanckriet, A. Lambrechts, B. de Moor, and J. Vandewalle. Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel Fisher discriminant analysis. Neural Computation, 14:1115-1147, 2002.
15. V. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.