Semiparametric support vector and linear programming machines

Smola, AJ; Friess, T; Schölkopf, B

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Semiparametric support vector and linear programming machines

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https://papers.nips.cc/paper/1575-semiparametric-support-vector-and-linear-programming-machines.pdf
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Smola, A., Friess, T., & Schölkopf, B. (1999). Semiparametric support vector and linear programming machines. In M. Kearns, S. Solla, & D. Cohn (Eds.), Advances in Neural Information Processing Systems 11 (pp. 585-591). Cambridge, MA, USA: MIT Press.

Cite as: https://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.