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Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods Support Vector Learning. pp 185–208. MIT Press, Cambridge (1999)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge. Book website: www.kernel-methods.net (2004)
Scholkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)
Lanckriet, G.R.G., Cristianini, N., Bartlett, P., El Ghaoui, L., Jordan, M.I.: Learning the Kernel Matrix with Semidefinite Programming. J. Mach. Learn. Res. 5, 27–72 (2004)
Joachims, T.: Text categorization with support vector machines. In: Proceedings of European Conference on Machine Learning (ECML) Chemnitz (1998)
Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: 7th International Conference on Information and Knowledge Management (1998)
LeCun, Y., Jackel, L.D., Bottou, L., Brunot, A., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Muller, U.A., Sackinger, E., Simard, P., Vapnik, V.: Comparison of learning algorithms for handwritten digit recognition. In: Fogelman-Soulie F., Gallinari P. (eds.), Proceedings International Conference on Artificial Neural Networks (ICANN) 2, 5360. EC2 (1995)
Jaakkola, T.S., Haussler, D.: Probabilistic kernel regression models. In: Proceedings of the 1999 Conference on AI and Statistics Fort Lauderdale (1999)
Brown, M., Grundy, W., Lin, D., Cristianini, N., Sugnet, C., Furey, T., Ares Jr., M., Haussler, D.: Knowledge-based analysis of mircoarray gene expression data using support vector machines. In: Proceedings of the National Academy of Sciences 97(1), 262–267 (2000)
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Cristianini, N., Ricci, E. (2008). Support Vector Machines. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30162-4_415
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DOI: https://doi.org/10.1007/978-0-387-30162-4_415
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