Semiparametric support vector and linear programming machines

Smola, AJ; Friess, T; Schölkopf, B; Kearns,; M.S.,; Solla, S.A.; Cohn, D.A.

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Semiparametric support vector and linear programming machines

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

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引用

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

引用: https://hdl.handle.net/11858/00-001M-0000-0013-E69D-4

要旨

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.