ML Anthology

Sex with Support Vector Machines

Baback Moghaddam, Ming-Hsuan Yang
NeurIPS 2000 pp. 960-966
/neurips/2000/moghaddam2000neurips-sex/

Abstract

Nonlinear Support Vector Machines (SVMs) are investigated for visual sex classification with low resolution "thumbnail" faces (21- by-12 pixels) processed from 1,755 images from the FE RET face database. The performance of SVMs is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Dis(cid:173) criminant, Nearest-Neighbor) as well as more modern techniques such as Radial Basis Function (RBF) classifiers and large ensemble(cid:173) RBF networks. Furthermore, the SVM performance (3.4% error) is currently the best result reported in the open literature.

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Cite

Text

Moghaddam and Yang. "Sex with Support Vector Machines." Neural Information Processing Systems, 2000.

Markdown

[Moghaddam and Yang. "Sex with Support Vector Machines." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/moghaddam2000neurips-sex/)

BibTeX

@inproceedings{moghaddam2000neurips-sex,
  title     = {{Sex with Support Vector Machines}},
  author    = {Moghaddam, Baback and Yang, Ming-Hsuan},
  booktitle = {Neural Information Processing Systems},
  year      = {2000},
  pages     = {960-966},
  url       = {https://mlanthology.org/neurips/2000/moghaddam2000neurips-sex/}
}