Semiparametric Support Vector and Linear Programming Machines

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Alex Smola, Thilo-Thomas Frieß, Bernhard Schölkopf


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 ma(cid:173) chines.