ML Anthology

Semiparametric Support Vector and Linear Programming Machines

Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf
NeurIPS 1998 pp. 585-591
/neurips/1998/smola1998neurips-semiparametric/

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 ma(cid:173) chines.

PDF NeurIPS Semantic Scholar

Cite

Text

Smola et al. "Semiparametric Support Vector and Linear Programming Machines." Neural Information Processing Systems, 1998.

Markdown

[Smola et al. "Semiparametric Support Vector and Linear Programming Machines." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/smola1998neurips-semiparametric/)

BibTeX

@inproceedings{smola1998neurips-semiparametric,
  title     = {{Semiparametric Support Vector and Linear Programming Machines}},
  author    = {Smola, Alex J. and Frieß, Thilo-Thomas and Schölkopf, Bernhard},
  booktitle = {Neural Information Processing Systems},
  year      = {1998},
  pages     = {585-591},
  url       = {https://mlanthology.org/neurips/1998/smola1998neurips-semiparametric/}
}