Kernel Design Using Boosting

NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems

Volumn , Issue , 2002, Pages 537-544

  Crammer, Koby ^a   Keshet, Joseph ^a   Singer, Yoram ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRUCTION PROCESS; DESIGN PROBLEMS; EMPIRICAL DATA; KERNEL CONSTRUCTION; KERNEL DESIGN; KERNEL OPERATOR; LEARNING KERNELS; SIMPLE KERNEL; SIMPLE++; WEAK LEARNER;

EID: 85156254642     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. V. N. Vapnik. Statistical Learning Theory. Wiley, 1998.
2. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press, 2000.
3. Huma Lodhi, John Shawe-Taylor, Nello Cristianini, and Christopher J. C. H. Watkins. Text classification using string kernels. Journal of Machine Learning Research, 2:419-444, 2002.
4. C. Leslie, E. Eskin, and W. Stafford Noble. The spectrum kernel: A string kernel for svm protein classification. In Proceedings of the Pacific Symposium on Biocomputing, 2002.
5. Nello Cristianini, Andre Elisseeff, John Shawe-Taylor, and Jaz Kandla. On kernel target alignment. In Advances in Neural Information Processing Systems 14, 2001.
6. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. Jordan. Learning the kernel matrix with semi-definite programming. In Proc. of the 19th Intl. Conf. on Machine Learning, 2002.
7. Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28(2):337-374, April 2000.
8. Michael Collins, Robert E. Schapire, and Yoram Singer. Logistic regression, adaboost and bregman distances. Machine Learning, 47(2/3):253-285, 2002.
9. Llew Mason, Jonathan Baxter, Peter Bartlett, and Marcus Frean. Functional gradient techniques for combining hypotheses. In Advances in Large Margin Classifiers. MIT Press, 1999.
10. Roger A. Horn and Charles R. Johnson. Matrix Analysis. Cambridge University Press, 1985.
11. F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65:386-407, 1958.
12. B. Schölkopf, S. Mika, C.J.C. Burges, P. Knirsch, K. Müller, G. Rätsch, and A.J. Smola. Input space vs. feature space in kernel-based methods. IEEE Trans. on NN, 10(5):1000-1017, 1999.
13. O. Bosquet and D.J.L. Herrmann. On the complexity of learning the kernel matrix. NIPS, 2002.
14. C.S. Ong, A.J. Smola, and R.C. Williamson. Superkenels. NIPS, 2002.