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" In B. Scholkopf, CJC Burges, and AJ Smola, editors, Advances in Kernel Methods — Support Vector Learning, pages 69-88, Cambridge, MA, 1999b. "
Advances in Large Margin Classifiers - Page 406
edited by - 2000 - 412 pages
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Advances in Neural Information Processing Systems 12 ..., Volumes 1-2

Sara A. Solla, Klaus-Robert Müller, Todd K. Leen - 2000 - 1124 pages
...England, 1988. [12] B. Schölkopf. Support vector learning. Oldenbourg Verlag, 1997. [13] B. Schölkopf, CJC Burges, and AJ Smola, editors. Advances in Kernel Methods Support Vector Learning. MIT Press, 1999. [14] B. Schölkopf, AJ Smola, and K.-R. Müller. Nonlinear component analysis as a...
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Handbook of Massive Data Sets

James Abello, Panos M. Pardalos, Mauricio G.C. Resende - 2002 - 1244 pages
...Society, 36:111—147, 1974. VN Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, 1995. G. Wahba. Support vector machines, reproducing kernel Hilbert spaces and the randomized GACV. In B. Schölkopf, CJC Burges, and AJ Smola, editors, Advances in Kernel Methods - Support Vector Learning,...
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Advances in Neural Information Processing Systems ..., Volume 14, Issues 1-2

Thomas G. Dietterich, Suzanna Becker, Professor of Information Engineering Zoubin Ghahramani, Zoubin Ghahramani - 2002 - 856 pages
...313-330. 1 15| Scholkopf, B., Smola, A., and Muller. K.-R. (1999). Kernel principal component analysis. In B. Scholkopf, CJC Burges, and AJ Smola, editors. Advances in Kernel Methods - SV Learning, pages 327-352. MIT Press, Cambridge, MA. 1 16] Watkins, C. (2000). Dynamic alignment...
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Bayesian Field Theory

Jörg C. Lemm - 2003 - 442 pages
...B, 45, 1983. [507] G. Wahba. Spline Models for Observational Data. SIAM, Philadelphia, 1990. [508] G. Wahba. Support vector machines, reproducing kernel...Hilbert spaces and the randomized GACV. In B. Scholkopf, C. Burges, and AJ Smola, editors, Advances in Kernel Methods: Support Vector Machines, pages 69 88....
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Design and Application of Hybrid Intelligent Systems

Ajith Abraham, Mario Köppen, Katrin Franke - 2003 - 1160 pages
...Cortes and V. Vapnik, "Support vector networks," Machine Learning, vol. 20, pp. 273 - 297, 1995. [11] G. Wahba, "Support vector machines, reproducing kernel Hilbert spaces and the randomized GACV," in Advances in Kernel Methods — Support Vector Learning (B. Scholkopf, CJC Surges, and AJ Smola, eds.),...
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Proceedings of the Fourth SIAM International Conference on Data Mining

Michael W. Berry - 2004 - 556 pages
...Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, CJC Burges, and AJ Smola, editors, Advances in Kernel Methods: Support Vector Learning. MIT Press, 1999. [28] B. Schölkopf, С. Burges, and V. Vapnik. Extracting support data for a given...
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Support Vector Machines for Pattern Classification

Shigeo Abe - 2005 - 362 pages
...Conference on Artificial Neural Networks (ICANN '98), volume 2, pages 111-6, Skovde, Sweden, 1998. 212. B. Scholkopf, CJC Burges, and AJ Smola, editors. Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge, MA, 1999. 213. B. Scholkopf, J. Shawe-Taylor, AJ Smola, and RC Williamson. Kerneldependent...
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Signal and Image Processing for Remote Sensing

C.H. Chen - 2006 - 712 pages
...support vector machine, IEEE Transactions on Neural Networks, 11(5), 1162-1173. 23. Wahba, G. (1999), Support vector machines, reproducing kernel Hilbert spaces and the randomized GACV, In Advances in Kernel Methods — Support Vector Learning, Scholkopf, Burges and Smola, eds., MIT press,...
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Handbook of Neural Engineering

Metin Akay - 2007 - 800 pages
...51:1147-1155, 2004. 31. V. VAPNIK. The Nature of Statistical Learning Theory. Springer, New York, 1995. 32. G. WAHBA. Support vector machines, reproducing kernel Hilbert spaces and the randomized GACV. In B. SCHAOLKOPF, C. BURGES, AND AJ SMOLA, Eds., Advances in Kernel Methods — Support Vector Learning....
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Epidemiology and Medical Statistics

2007 - 870 pages
...Observational Data, SIAM. CBMS-NSF Regional Conference Series in Applied Mathematics, v. 59. Wahba, G. (1999). Support vector machines, reproducing kernel Hilbert spaces and the randomized GACV. In: Scholkopf, B., Burges, C., Smola, A. (Eds.), Advances in Kernel Methods - Support Vector Learning....
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