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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. In M. Kearns, S. Solla, & D. Cohn (Eds.), Advances in Neural Information Processing Systems 11 (pp. 585-591). Cambridge, MA, USA: MIT Press.

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 Creators:
Smola, AJ1, Author              
Friess, T, Author
Schölkopf, B1, Author              
Affiliations:
1External Organizations, ou_persistent22              

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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: 1999-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: 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
Start-/End Date: 1998-11-30 - 1998-12-05

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Title: Advances in Neural Information Processing Systems 11
Source Genre: Proceedings
 Creator(s):
Kearns, MJ, Editor
Solla, SA, Editor
Cohn, DA, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 585 - 591 Identifier: DOI: 0-262-11245-0