ML Anthology

Kernel Methods for Implicit Surface Modeling

Joachim Giesen, Simon Spalinger, Bernhard Schölkopf
NeurIPS 2004 pp. 1193-1200
/neurips/2004/giesen2004neurips-kernel/

Abstract

We describe methods for computing an implicit model of a hypersurface that is given only by a finite sampling. The methods work by mapping the sample points into a reproducing kernel Hilbert space and then deter- mining regions in terms of hyperplanes.

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Cite

Text

Giesen et al. "Kernel Methods for Implicit Surface Modeling." Neural Information Processing Systems, 2004.

Markdown

[Giesen et al. "Kernel Methods for Implicit Surface Modeling." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/giesen2004neurips-kernel/)

BibTeX

@inproceedings{giesen2004neurips-kernel,
  title     = {{Kernel Methods for Implicit Surface Modeling}},
  author    = {Giesen, Joachim and Spalinger, Simon and Schölkopf, Bernhard},
  booktitle = {Neural Information Processing Systems},
  year      = {2004},
  pages     = {1193-1200},
  url       = {https://mlanthology.org/neurips/2004/giesen2004neurips-kernel/}
}