Glasmachers T, Igel C. (2005). Gradient-based adaptation of general gaussian kernels. Neural computation. 17 [PubMed]

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References and models cited by this paper

Baker A. (2002). Matrix groups: An introduction to Lie group theory.

Chung KM, Kao WC, Sun CL, Wang LL, Lin CJ. (2003). Radius margin bounds for support vector machines with the RBF kernel. Neural computation. 15 [PubMed]

Galleske I, Castellanos J. (2002). Optimization of the kernel functions in a probabilistic neural network analyzing the local pattern distribution. Neural Comput. 14

Gold C, Sollich P. (2003). Model selection for support vector machine classification Neurocomputing. 55

Igel C, Friedrichs F. (2005). Evolutionary tuning of multiple SVM parameters Neurocomputing. 64

Jaakkola T, Diekhans M, Haussler D. (1999). Using the Fisher kernel method to detect remote protein homologies. Proceedings. International Conference on Intelligent Systems for Molecular Biology. [PubMed]

Keerthi SS. (2002). Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE transactions on neural networks. 13 [PubMed]

Vapnik V. (1998). Statistical Learning Theory.

Vapnik V, Mukherjee S, Chapelle O, Bousquet O. (2002). Choosing multiple parameters for support vector machines Machine Learning. 46

Vapnik V, Scholkopf B, Burges CJC. (1995). Extracting support data for a given task Proceedings of the First International Conference on Knowledge Discovery and Data Mining .

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Glasmachers T, Igel C. (2008). Second-order SMO improves SVM online and active learning. Neural computation. 20 [PubMed]

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