A Parallel Mixture of SVMs for Very Large Scale Problems

Neural Computation

Volumn 14, Issue 5, 2002, Pages 1105-1114

  Collobert, Ronan ^a,b   Bengio, Samy ^a   Bengio, Yoshua ^b  

^a IDIAP RESEARCH INSTITUTE   (Switzerland)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER PROGRAM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; SOFTWARE;

EID: 0036583160     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976602753633402     Document Type: Article

References (9)

1. Collobert, R., & Bengio, S. (2001). SVMTorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research, 1, 143-160.
2. Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297.
3. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3(1), 79-87.
4. Joachims, T. (1999). Making large-scale support vector machine learning practical. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods. Cambridge, MA: MIT Press.
5. Kwok, J. T. (1998). Support vector mixture for classification and regression problems. In Proceedings of the International Conference on Pattern Recognition (ICPR) (pp. 255-258). Brisbane, Queensland, Australia.
6. Osuna, E., Freund, R., & Girosi, F. (1997). Training support vector machines: An application to face detection. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 130-136). San Juan, Puerto Rico.
7. Platt, J. (1999). Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods. Cambridge, MA: MIT Press.
8. Rida, A., Labbi, A., & Pellegrini, C. (1999). Local experts combination trough density decomposition. In International Workshop on AI and Statistics (Uncertainty '99). San Mateo, CA: Morgan Kaufmann.
9. Vapnik, V. N. (1995). The nature of statistical learning theory. (2nd ed). New York: Springer-Verlag.