Hyperkerneis

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Ong, Cheng Soon ^a   Smola, Alexander J ^a   Williamson, Robert C ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION;

GAUSSIAN PROCESSES; KERNEL SELECTION; PARAMETERIZED; REPRESENTER THEOREM; REPRODUCING KERNEL HILBERT SPACES; STATISTICAL ESTIMATION;

RISK PERCEPTION;

EID: 84899013191     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. Jordan. Learning the kernel matrix with semidefinite programming. In ICML. Morgan Kaufmann, 2002.
2. C. K. I. Williams. Prediction with Gaussian processes: From linear regression to linear prediction and beyond. In M. I. Jordan, editor, Learning and Inference in Graphical Models. Kluwer Academic, 1998.
3. O. Chapelle, V Vapnik, O. Bousquet, and S. Mukherjee. Choosing kernel parameters for support vector machines. Machine Learning, 2002. Forthcoming.
4. G. Wahba. Spline Models for Observational Data, Volume 59 of CBMS-NSF Regional Conference Series in Applied Mathematics. SIAM, Philadelphia, 1990.
5. K. Crammer, J. Keshet, and Y. Singer. Kernel design using boosting, In Advances in Neural Information Processing Systems 15, 2002. In press.
6. O. Bousquet and D. Herrmann. On the complexity of learning the kernel matrix. In Advances in Neural Information Processing Systems 15, 2002. In press.
7. N. Cristianini, A. Elisseeff, and J. Shawe-Taylor. On optimizing kernel alignment. Technical Report NC2-TR-2001-087, Neuro COLT, http://www.neurocolt. com, 2001.
8. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2002.
9. S. Fine and K. Scheinberg. Efficient SVM training using low-rank kernel representation. Technical report, IBM Watson Research Center, New York, 2000.
10. Y Freund and R. E. Schapire. Experiments with a new boosting algorithm. In ICML, pages 148-146. Morgan Kaufmann Publishers, 1996.
11. G. Ratsch, T. Onoda, and K. R. Müller. Soft margins for adaboost. Machine Learning, 42(3):287-320, 2001.