On the optimal parameter choice for v-support vector machines

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 25, Issue 10, 2003, Pages 1274-1284

  Steinwart, Ingo ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

Author keywords

PAC model; Parameter selection; Pattern recognition; Support vector machines

Indexed keywords

ALGORITHMS; ASYMPTOTIC STABILITY; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; PATTERN RECOGNITION; PROBABILITY DISTRIBUTIONS; THEOREM PROVING;

GAUSSIAN KERNEL; PARAMETER SELECTION; SUPPORT VECTOR MACHINES;

NEURAL NETWORKS;

EID: 0142039767     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2003.1233901     Document Type: Article

References (19)

1. C.-C. Chang and C.-J. Lin, "Training Nu-Support Vector Classifiers: Theory and Algorithms, Neural Computation, vol. 13, pp 2119-2147, 2001.
2. C.-C. Chang and C.-J. Lin LIBSVM 2.33, http://www.csie.ntu.du.tw/cjlin/. 2002.
3. N. Cristianini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge Univ. Press, 2000.
4. F. Cucker and S. Smale, "On the Mathematical Foundations of Learning," Bulletin Am. Math. Soc., vol. 39, pp. 1-49, 2002.
5. W. Dahmen and C.A. Micchelli, "Some Remarks on Ridge Functions," Approximate Theory Application, vol. 3, pp. 139-143, 1987.
6. L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, New York: Springer, 1997.
7. R.M. Dudley, "A Course on Empirical Processes," Lecture Notes in Math., vol. 1097, pp. 1-142, 1984.
8. R.M. Dudley, Real Analysis and Probability. New York: Chapman Hall, 1989.
9. T. Evgeniou, M. Pontil, and T. Poggio, "Regularization Networks and Support Vector Machines," Advances in Computational Mathematics, vol. 13, pp. 1-50, 2000.
10. M. Loève, Probability Theory, third ed., Princeton: D. van Nostrand, 1963.
11. G. Rätsch http://mlg.anu.edu.au/~raetsch/data/index.html. 2003.
12. G. Rätsch, T. Onoda, and K.-R. Müller, "Soft Margins for AdaBoost," Machine Learning, vol. 42, pp. 287-320, 2001.
13. G. Rätsch, Oral Communication.
14. B. Schölkopf, A.J. Smola, R.C. Williamson, and P.L. Bartlett, "New Support Vector Algorithms," Neural Computation, vol. 12, pp. 1207-1245, 2000.
15. B. Schölkopf and A.J. Smola, Learning with Kernels. MIT Press, 2001.
16. I. Steinwart, "On the Influence of the Kernel on the Consistency of Support Vector Machines," J. Machine Learning Research, vol. 2, pp. 67-93, 2001.
17. I. Steinwart, "Support Vector Machines Are Universally Consistent," J. Complexity, vol. 18, pp. 768-791, 2002.
18. I. Steinwart, "Consistency of Support Vector Machines and Other Regularized Kernel Machines," to be published in IEEE Trans. Information Theory, pending publication.
19. I. Steinwart, "Sparseness of Support Vector Machines," J. Machine Learning Research, to be published, 2003