Simpler knowledge-based support vector machines

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 521-528

  Le, Quoc V ^a   Smola, Alex J ^b   Gärtner, Thomas ^c  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b CSIRO   (Australia)

^c FRAUNHOFER IAIS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; OPTIMIZATION; PREDICTIVE CONTROL SYSTEMS; PROBLEM SOLVING;

CONSTRAINED CONCAVE CONVEX PROCEDURE; OPTIMIZATION PROBLEMS; SUPPORT VECTOR MACHINES;

LEARNING SYSTEMS;

EID: 33749267288     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Aronszajn, N. (1950). Theory of reproducing kernels. Transactions of the American Mathematical Society, 68, 337-404.
2. Bartlett, P., & Mendelson, S. (2002). Rademacher and gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3, 463-482.
3. Bousquet, O., Boucheron, S., & Lugosi, G. (2004). Theory of classification: a survey of recent advances. ESAIM: Probability and Statistics. submitted.
4. DeCoste, D., & Schölkopf, B. (2002). Training invariant support vector machines. Machine Learning, 46, 161-190.
5. Also: Technical Report JPL-MLTR-00-1, Jet Propulsion Laboratory, Pasadena, CA, 2000.
6. Fung, G., Mangasarian, O., & Shavlik, J. (2000). Knowledge-based nonlinear kernel classifiers (Technical Report). Data Mining Institute.
7. Fung, G., Mangasarian, O., & Shavlik, J. (2002a). Knowledge-based support vector machine classifiers. Advances in Neural Information Processing Systems 15. MIT Press.
8. Fung, G., Mangasarian, O. L., & Smola, A. J. (2002b). Minimal kernel classifiers. Journal of Machine Learning Research, 3, 303-321.
9. Graepel, T., & Herbrich, R. (2004). Invariant pattern recognition by semidefinite programming machines. Advances in Neural Information Processing Systems 16. MIT Press.
10. Herbrich, R., & Williamson, R. C. (2002). Algorithmic luckiness. Journal of Machine Learning Research, 3, 175-212.
11. MacKay, D. J. C. (2003). Information theory, inference, and learning algorithms. Cambridge University Press.
12. Mammen, E., Marron, J., Turlach, B., & Wand, M. (2001). A general projection framework for constrained smoothing. Statistical Science, 16, 232-248.
13. Mangasarian, O. L. (2000). Generalized support vector machines. Advances in Large Margin Classifiers (pp. 135-146). Cambridge, MA: MIT Press.
14. Mangasarian, O. L., Shavlik, J. W., & Wild, E. W. (2004). Knowledge-based kernel approximation. Journal of Machine Learning Research, 5.
15. Mangasarian, O. L., & Wild, E. W. (2005). Nonlinear knowledge in kernel approximation (Technical Report). Data Mining Institute, University of Wisconsin.
16. Micchelli, C. A. (1986). Interpolation of scattered data: distance matrices and conditionally positive definite functions. Constructive Approximation, 2, 11-22.
17. Neal, R. (1996). Bayesian learning in neural networks. Springer.
18. Schölkopf, B., & Smola, A. (2002). Learning with kernels. Cambridge, MA: MIT Press.
19. Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New support vector algorithms. Neural Computation, 12, 1207-1245.
20. Smola, A. J., & Schölkopf, B. (1998). On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica, 22, 211-231.
21. Smola, A. J., Schölkopf, B., & Müller, K.-R. (1998). The connection between regularization operators and support vector kernels. Neural Networks, 11, 637-649.
22. Smola, A. J., Vishwanathan, S. V. N., & Hofmann, T. (2005). Kernel methods for missing variables. Proceedings of International Workshop on Artificial Intelligence and Statistics (pp. 325-332). Society for Artificial Intelligence and Statistics.
23. Tipping, M. E. (2000). The relevance vector machine. Advances in Neural Information Processing Systems 12 (pp. 652-658). Cambridge, MA: MIT Press.
24. Yuille, A., & Rangarajan, A. (2003). The concave-convex procedure. Neural Computation, 15, 915-936.