Kernel dependency estimation

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Weston, Jason ^a   Chapelle, Olivier ^a   Elisseeff, André ^a   Schölkopf, Bernhard ^a   Vapnik, Vladimir ^b  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b NEC LABORATORIES AMERICA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION; TREES (MATHEMATICS);

GENERAL CLASS; INPUT AND OUTPUTS; KERNEL FUNCTION; LEARNING PROBLEM; SIMILARITY MEASURE;

SELF ORGANIZING MAPS;

EID: 84898971943     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. N. Cristianini, A. Elisseeff, and J. Shawe-Taylor. On optimizing kernel alignment. Technical Report 2001-087, NeuroCOLT, 2001.
2. I. Frank and J. Friedman. A Statistical View of Some Chemometrics Regression Tools. Technometrics, 35(2): 109-147, 1993.
3. T. Graepel, R. Herbrich, P. Bollmann-Sdorra, and K. Obermayer. Classification on pairwise proximity data. NIPS, 11: 438-444, 1999.
4. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer-Verlag, New York, 2001.
5. D. Haussler. Convolutional kernels on discrete structures. Technical Report UCSC-CRL-99-10, Computer Science Department, University of California at Santa Cruz, 1999.
6. J. Li, A. N. Michel, and W. Porod. Analysis and synthesis of a class of neural networks: linear systems operating on a closed hypercube. IEEE Trans. on Circuits and Systems, 36(11): 1405-22, 1989.
7. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. Watkins. Text classification using string kernels. Journal of Machine Learning Research, 2: 419-444, 2002.
8. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.-R. Müller. Fisher discriminant analysis with kernels. In Y.-H. Hu, J. Larsen, E. Wilson, and S. Douglas, editors, Neural Networks for Signal Processing IX, pages 41-48. IEEE, 1999.
9. C. Saunders, V. Vovk, and A. Gammerman. Ridge regression learning algorithm in dual variables. In J. Shavlik, editor, Machine Learning Proceedings of the Fifteenth International Conference(ICML '98), San Francisco, CA, 1998. Morgan Kaufmann.
10. B. Schölkopf, S. Mika, C. Burges, P. Knirsch, K.-R. Müller, G. Rätsch, and A. J. Smola. Input space vs. feature space in kernel-based methods. IEEE-NN, 10(5): 1000-1017, 1999.
11. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
12. V. Vapnik. Statistical Learning Theory. John Wiley and Sons, New York, 1998.