Training SVM with indefinite kernels

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 136-143

  Chen, Jianhui ^a   Ye, Jieping ^a  

^a Arizona State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINED OPTIMIZATION; EFFICIENCY; ITERATIVE METHODS; LINEAR PROGRAMMING; MACHINE LEARNING; MATRIX ALGEBRA; BENCHMARKING; CONVERGENCE OF NUMERICAL METHODS; LEARNING SYSTEMS; ROBOT LEARNING; SUPPORT VECTOR MACHINES;

CONVERGENCE PROPERTIES; GLOBAL OPTIMUM SOLUTIONS; INDEFINITE KERNEL; MULTIPLE KERNEL LEARNING; NOISY OBSERVATIONS; POSITIVE SEMIDEFINITE; PRUNING STRATEGY; TRAINING SUPPORT VECTOR MACHINES;

SUPPORT VECTOR MACHINES; CONSTRAINED OPTIMIZATION;

BENCHMARK DATUM; CONVERGENCE PROPERTIES; GLOBAL OPTIMUM SOLUTIONS; INDEFINITE KERNELS; KERNEL MACHINES; LINEAR PROGRAMS; MULTIPLE KERNEL LEARNING; NOISY OBSERVATIONS; PRUNING STRATEGIES; SEMI INFINITES; SUPPORT VECTORS; TRAINING SUPPORT VECTOR MACHINES;

EID: 56449083666     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390174     Document Type: Conference Paper

References (29)

1. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
2. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press.
3. Golub, G. H., & Van Loan, C. F. (1996). Matrix computations. Johns Hopkins University Press. 3 edition.
4. Graepel, T., Herbrich, R., Bollmann-Sdorra, P., & Obermayer, K. (1998). Classification on pairwise proximity data. NIPS (pp. 438-444).
5. Haasdonk, B. (2005). Feature space interpretation of svms with indefinite kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 482-492.
6. Hettich, R., & Kortanek, K. O. (1993a). Semi-infinite programming: theory, methods, and applications. SIAM Review, 35, 380-429.
7. Hettich, R., & Kortanek, K. O. (1993b). Semi-infinite programming: Theory, methods, and applications. SIAM Review, 35, 380-429.
8. Hiriart-Urruty, J.-B., & Lemarechal, C. (1993). Convex analysis and minimization algorithms II. Springer.
9. Hull, J. J. (1994). A database for handwritten text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 550-554.
10. Kondor, R. I., & Lafferty, J. D. (2002). Diffusion kernels on graphs and other discrete input spaces. ICML (pp. 315-322).
11. Lanckriet, G. R. G., Cristianini, N., Bartlett, P. L., Ghaoui, L. E., & Jordan, M. I. (2004). Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5, 27-72.
12. Lin, H.-T., & Lin, C.-J. (2003). A study on sigmoid kernels for svm and the training of non-psd kernels by smo-type methods. Technical Report, National Taiwan University.
13. Luss, R., & d'Aspremont, A. (2007). Support vector machine classification with indefinite kernels. NIPS.
14. Newman, D., Hettich, S., Blake, C., & Merz, C. (1998). UCl repository of machine learning databases.
15. Nocedal, J., & Wright, S. J. (1999). Numerical optimization springer series in operations research. Springer.
16. Ong, C. S., Mary, X., Canu, S., & Smola, A. J. (2004). Learning with non-positive kernels. ICML.
17. Pekalska, E., Paclik, P., & Duin, R. P. W. (2002). A generalized kernel approach to dissimilarity-based classification. Journal of Machine Learning Research, 2, 175-211.
18. Qamra, A., Meng, Y., & Chang, E. Y. (2005). Enhanced perceptual distance functions and indexing for image replica recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 379-391.
19. Rakotomamonjy, A., Bach, F., Canu, S., & Grandvalet, Y. (2007). More efficiency in multiple kernel learning. ICML.
20. Roth, V., Laub, J., Kawanabe, M., & Buhmann, J. M. (2003a). Optimal cluster preserving embedding of nonmetric proximity data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1540-1551.
21. Roth, V., Laub, J., Kawanabe, M., & Buhmann, J. M. (2003b). Optimal cluster preserving embedding of nonmetric proximity data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1540-1551.
22. Schölkopf, B., & Smola, A. J. (2001). Learning with kernels: Support vector machines, regularization, optimization, and beyond. The MIT Press.
23. Shimodaira, H., Noma, K.-I., Nakai, M., & Sagayama, S. (2001). Dynamic time-alignment kernel in support vector machine. NIPS (pp. 921-928).
24. Smola, A. J., Óvári, Z. L., & Williamson, R. C. (2000). Regularization with dot-product kernels. NIPS (pp. 308-314).
25. Sonnenburg, S., Rätsch, G., Schäfer, C., & Schölkopf, B. (2006). Large scale multiple kernel learning. Journal of Machine Learning Research, 7, 1531-1565.
26. Teo, C. H., Smola, A., Vishwanathan, S. V., & Le, Q. V. (2007). A scalable modular convex solver for regularized risk minimization. KDD (pp. 727-736).
27. Tong, S., & Chang, E. (2001). Support vector machine active learning for image retrieval. ACM Multimedia (pp. 107-118).
28. Vapnik, V. N. (1995). The nature of statistical learning theory. New York, NY, USA: Springer-Verlag New York, Inc.
29. Wu, G., Zhang, Z., & Chang, E. Y. (2005). An analysis of transformation on non-positive semidefinite similarity matrix for kernel machines. Technical Report, UCSB.