A tutorial on ν‐support vector machines

Chen, P-H; Lin, C-J; Schölkopf, B

doi:10.1002/asmb.537

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A tutorial on ν‐support vector machines

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

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https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.537
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Chen, P.-H., Lin, C.-J., & Schölkopf, B. (2005). A tutorial on ν‐support vector machines. Applied Stochastic Models in Business and Industry, 21(2), 111-136. doi:10.1002/asmb.537.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D6A1-A

Abstract

We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called -SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley Sons, Ltd.