Model selection for Support Vector Machines

Advances in Neural Information Processing Systems

Volumn , Issue , 2000, Pages 230-236

  Chapell, Clivier ^a,b   Vapnik, Vladimir ^a  

^a AT AND T LABS RESEARCH   (United States)

^b UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BEST CHOICE; FEATURE SPACE; FUNCTIONALS; MODEL SELECTION; RELATIVE QUALITIES; RESCALING; SUPPORT VECTOR;

SUPPORT VECTOR MACHINES;

EID: 84899010634     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167, 1998.
2. T. S. Jaakkola and D. Haussier. Probabilistic kernel regression models. In Proceedings of the 1999 Conference on AI and Statistics, 1999.
3. M. Opper and O. Winther. Gaussian process classification and SVM: Mean field results and leave-one-out estimator. In Advances in Large Margin Classifiers. MIT Press, 1999. to appear.
4. B. Scholkopf, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Kernel-dependent Support Vector error bounds. In Ninth International Conference on Artificial Neural Networks, pp. 304-309
5. B. Scholkopf, A. Smola, and K.-R. Müller. Kernel principal component analysis. In Artificial Neural Networks-ICANN97, pages 583-588, Berlin, 1997. Springer Lecture Notes in Computer Science, Vol. 1327.
6. V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
7. V. Vapnik and O. Chapelle. Bounds on error expectation for SVM. Neural Computation, 1999. Submitted.