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Conference Paper

Semiparametric support vector and linear programming machines

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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

Smola, A., Friess, T., & Schölkopf, B. (1999). Semiparametric support vector and linear programming machines. Advances in Neural Information Processing Systems, 585-591.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E69D-4
Abstract
Semiparametric models are useful tools in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. We extend two learning algorithms - Support Vector machines and Linear Programming machines to this case and give experimental results for SV machines.