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  Semiparametric support vector and linear programming machines

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

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E69D-4 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E69E-2
Genre: Conference Paper

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 Creators:
Smola, AJ, Author
Friess, T, Author
Schölkopf, B1, Author           
Kearns, Editor
M.S., Editor
Solla, S.A., Editor
Cohn, D.A., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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.

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 Dates: 1999-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-11245-0
URI: http://books.nips.cc/nips11.html
BibTex Citekey: 804
 Degree: -

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Title: Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998)
Place of Event: Denver, CO, USA
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Title: Advances in Neural Information Processing Systems
Source Genre: Journal
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 585 - 591 Identifier: -