The One Class Support Vector Machine Solution Path

@article{Lee2007TheOC,
  title={The One Class Support Vector Machine Solution Path},
  author={Gyemin Lee and Clayton Scott},
  journal={2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07},
  year={2007},
  volume={2},
  pages={II-521-II-524}
}
This paper applies the algorithm of Hastie et al., (2004) to the problem of learning the entire solution path of the one class support vector machine (OC-SVM) as its free parameter ν varies from 0 to 1. The OC-SVM with Gaussian kernel is a nonparametric estimator of a level set of the density governing the observed sample, with the parameter ν implicitly defining the corresponding level. Thus, the path algorithm produces estimates of all level sets and can therefore be applied to a variety of… CONTINUE READING
Highly Cited
This paper has 30 citations. REVIEW CITATIONS

From This Paper

Topics from this paper.
21 Citations
11 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 21 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 11 references

Kolacyzk, “Simulataneous nonparametric annotation of contaminated multivariate data,”

  • E. C. Scott
  • 2007
1 Excerpt

“SvmPath: t the entire regularization path for the SVM,”

  • T. Hastie
  • http://www-stat.stanford.edu/ hastie/Papers…
  • 2004
1 Excerpt

Learning with Kernels, chapter 7, pp. 208–209

  • B. Schölkopf, A. J. Smola
  • 2002
2 Excerpts

Similar Papers

Loading similar papers…