New support vector algorithms

Neural Computation

Volumn 12, Issue 5, 2000, Pages 1207-1245

  Schölkopf, Bernhard ^a,b,c   Smola, Alex J ^a,b   Williamson, Robert C ^b   Bartlett, Peter L ^b  

^a FRAUNHOFER FIRST   (Germany)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 17444438778     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976600300015565     Document Type: Article

References (41)

1. Aizerman, M., Braverman, E., & Rozonoer, L. (1964). Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, 25, 821-837.
2. Anthony, M., & Bartlett, P. L. (1999). Neural network learning: Theoretical foundations. Cambridge: Cambridge University Press.
3. Bartlett, P. L. (1998). The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 44(2), 525-536.
4. Bertsekas, D. P. (1995). Nonlinear programming. Belmont, MA: Athena Scientific.
5. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In D. Haussler (Ed.), Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory (pp. 144-152). Pittsburgh, PA: ACM Press.
6. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 1-47.
7. Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297.
8. Girosi, F. (1998). An equivalence between sparse approximation and support vector machines. Neural Computation, 10(6), 1455-1480.
9. Horn, R. A., & Johnson, C. R. (1985). Matrix analysis. Cambridge: Cambridge University Press.
10. Huber, P. J. (1981). Robust statistics. New York: Wiley.
11. Mattera, D., & Haykin, S. (1999). Support vector machines for dynamic reconstruction of a chaotic system. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods - Support vector learning (pp. 211-241). Cambridge, MA: MIT Press.
12. Müller, K.-R., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., and Vapnik, V. (1999). Predicting time series with support vector machines. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods - Support vector learning (pp. 243-253). Cambridge, MA: MIT Press.
13. Murata, N., Yoshizawa, S., & Amari, S. (1994). Network information criterion -determining the number of hidden units for artificial neural network models. IEEE Transactions on Neural Networks, 5, 865-872.
14. Osuna, E., & Girosi, F. (1999). Reducing run-time complexity in support vector machines. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods - Support vector learning (pp. 271-283). Cambridge, MA: MIT Press.
15. Platt, J. (1999). Fast training of SVMs using sequential minimal optimization. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods-Support vector learning (pp. 185-208). Cambridge, MA: MIT Press.
16. Pontil, M., Rifkin, R., & Evgeniou, T. (1999). From regression to classification in support vector machines. In M. Verleysen (Ed.), Proceedings ESANN (pp. 225-230). Brussels: D Facto.
17. Schölkopf, B. (1997). Support vector learning. Munich: R. Oldenbourg Verlag.
18. Schölkopf, B., Bartlett, P. L., Smola, A., & Williamson, R. C. (1998). Support vector regression with automatic accuracy control. In L. Niklasson, M. Bodén, & T. Ziemke (Eds.), Proceedings of the 8th International Conference on Artificial Neural Networks (pp. 111-116). Berlin: Springer-Verlag.
19. Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999). Advances in kernel methods-Support vector learning. Cambridge, MA: MIT Press.
20. Schölkopf, B., Burges, C., & Vapnik, V. (1995). Extracting support data for a given task. In U. M. Fayyad & R. Uthurusamy (Eds.), Proceedings, First International Conference on Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press.
21. Schölkopf, B., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Kernel-dependent support vector error bounds. In Ninth International Conference on Artificial Neural Networks (pp. 103-108). London: IEE.
22. Schölkopf, B., Smola, A., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299-1319.
23. Schölkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T., & Vapnik, V. (1997). Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans. Sign. Processing, 45, 2758-2765.
24. Seifert, B., Gasser, T., & Wolf, A. (1993). Nonparametric estimation of residual variance revisited. Biometrika, 80, 373-383.
25. Shawe-Taylor, J., Bartlett, P. L., Williamson, R. C., & Anthony, M. (1998). Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory, 44(5), 1926-1940.
26. Shawe-Taylor, J., & Cristianini, N. (1999). Margin distribution bounds on generalization. In Computational Learning Theory: 4th European Conference (pp. 263-273). New York: Springer.
27. Smola, A. J. (1998). Learning with kernels. Doctoral dissertation, Technische Universität Berlin. Also: GMD Research Series No. 25, Birlinghoven, Germany.
28. Smola, A. J. (1998). Learning with kernels. Doctoral dissertation, Technische Universität Berlin. Also: GMD Research Series No. 25, Birlinghoven, Germany.
29. Smola, A., Frieß, T., & Schölkopf, B. (1999). Semiparametric support vector and linear programming machines. In M. S. Kearns, S. A. Solla, & D. A. Cohn (Eds.), Advances in neural information processing systems, 11 (pp. 585-591). Cambridge, MA: MIT Press.
30. Smola, A., Murata, N., Schölkopf, B., & Müller, K.-R. (1998). Asymptotically optimal choice of ε-loss for support vector machines. In L. Niklasson, M. Bodén, & T. Ziemke (Eds.), Proceedings of the 8th International Conference on Artificial Neural Networks (pp. 105-110). Berlin: Springer-Verlag.
31. Smola, A., & Schölkopf, B. (1998). On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica, 22, 211-231.
32. Smola, A., Schölkopf, B., & Müller, K.-R. (1998). The connection between regularization operators and support vector kernels. Neural Networks, 11, 637-649.
33. Smola, A., Williamson, R. C., Mika, S., & Schölkopf, B. (1999). Regularized principal manifolds. In Computational Learning Theory: 4th European Conference (pp. 214-229). Berlin: Springer-Verlag.
34. Stitson, M., Gammerman, A., Vapnik, V., Vovk, V., Watkins, C., & Weston, J. (1999). Support vector regression with ANOVA decomposition kernels. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods-Support vector learning (pp. 285-291). Cambridge, MA: MIT Press.
35. Vapnik, V. (1979). Estimation of dependences based on empirical data [in Russian]. Nauka: Moscow. (English translation: Springer-Verlag, New York, 1982).
36. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
37. Vapnik, V., & Chervonenkis, A. (1974). Theory of pattern recognition [in Russian]. Nauka: Moscow. (German Translation: W. Wapnik & A. Tscherwonenkis, Theorie der Zeichenerkennung, Akademie-Verlag, Berlin, 1979).
38. Vapnik, V., & Chervonenkis, A. (1974). Theory of pattern recognition [in Russian]. Nauka: Moscow. (German Translation: W. Wapnik & A. Tscherwonenkis, Theorie der Zeichenerkennung, Akademie-Verlag, Berlin, 1979).
39. Wahba, G. (1999). Support vector machines, reproducing kernel Hubert spaces and the randomized GACV. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods - Support vector learning (pp. 69-88). Cambridge, MA: MIT Press.
40. White, H. (1994). Parametric statistical estimation with artificial neural networks: A condensed discussion. In V. Cherkassky, J. H. Friedman, & H. Wechsler (Eds.), From statistics to neural networks. Berlin: Springer.
41. Williamson, R. C., Smola, A. J., & Schölkopf, B. (1998). Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators (Tech. Rep. 19 Neurocolt Series). London: Royal Holloway College. Available online at http://www.neurocolt.com.