ML Anthology

Nonparametric Kernel Estimators for Image Classification

Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider
CVPR 2012 pp. 2989-2996
doi:10.1109/CVPR.2012.6248028 /cvpr/2012/poczos2012cvpr-nonparametric/

Abstract

We introduce a new discriminative learning method for image classification. We assume that the images are represented by unordered, multi-dimensional, finite sets of feature vectors, and that these sets might have different cardinality. This allows us to use consistent nonparametric divergence estimators to define new kernels over these sets, and then apply them in kernel classifiers. Our numerical results demonstrate that in many cases this approach can outperform state-of-the-art competitors on both simulated and challenging real-world datasets.

PDF CVPR Semantic Scholar

Cite

Text

Póczos et al. "Nonparametric Kernel Estimators for Image Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6248028

Markdown

[Póczos et al. "Nonparametric Kernel Estimators for Image Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/poczos2012cvpr-nonparametric/) doi:10.1109/CVPR.2012.6248028

BibTeX

@inproceedings{poczos2012cvpr-nonparametric,
  title     = {{Nonparametric Kernel Estimators for Image Classification}},
  author    = {Póczos, Barnabás and Xiong, Liang and Sutherland, Danica J. and Schneider, Jeff G.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2012},
  pages     = {2989-2996},
  doi       = {10.1109/CVPR.2012.6248028},
  url       = {https://mlanthology.org/cvpr/2012/poczos2012cvpr-nonparametric/}
}