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Journal Article

New Support Vector Algorithms

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Schölkopf, B., Smola AJ, Williamson, R., & Bartlett, P. (2000). New Support Vector Algorithms. Neural Computation, 12(5), 1207-1245. doi:doi:10.1162/089976600300015565.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E4E5-0
Abstract
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter nu} lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter {epsilon} in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of {nu, and report experimental results.