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Semiparametric support vector and linear programming machines

1999

Conference Paper

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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.

Author(s): Smola, AJ. and Friess, T. and Schölkopf, B.
Book Title: Advances in Neural Information Processing Systems 11
Journal: Advances in Neural Information Processing Systems
Pages: 585-591
Year: 1999
Month: June
Day: 0
Editors: MS Kearns and SA Solla and DA Cohn
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998)
Event Place: Denver, CO, USA

Address: Cambridge, MA, USA
Digital: 0
Institution: Royal Holloway College
ISBN: 0-262-11245-0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{804,
  title = {Semiparametric support vector and linear programming machines},
  author = {Smola, AJ. and Friess, T. and Sch{\"o}lkopf, B.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 11},
  pages = {585-591 },
  editors = {MS Kearns and SA Solla and DA Cohn},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  institution = {Royal Holloway College},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {1999},
  month_numeric = {6}
}