A tutorial on v-support vector machines

Chen, P-H; Lin, C-J; Schölkopf, B

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A tutorial on v-support vector machines

Chen, P.-H., Lin, C.-J., & Schölkopf, B. (2005). A tutorial on v-support vector machines. Applied Stochastic Models in Business and Industry, 21(2), 111-136.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D6A1-A Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D6A2-8

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Chen, P-H, Author
Lin, C-J, Author
Schölkopf, B¹, Author

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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795

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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.

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Dates: Date issued: 2005

Publication Status: Issued

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Identifiers: BibTex Citekey: 3353

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Title: Applied Stochastic Models in Business and Industry

Source Genre: Journal

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Pages: - Volume / Issue: 21 (2) Sequence Number: - Start / End Page: 111 - 136 Identifier: -