Support Vector Method for Multivariate Density Estimation
Abstract
A new method for multivariate density estimation is developed based on the Support Vector Method (SVM) solution of inverse ill-posed problems. The solution has the form of a mixture of den(cid:173) sities. This method with Gaussian kernels compared favorably to both Parzen's method and the Gaussian Mixture Model method. For synthetic data we achieve more accurate estimates for densities of 2, 6, 12, and 40 dimensions.
Cite
Text
Vapnik and Mukherjee. "Support Vector Method for Multivariate Density Estimation." Neural Information Processing Systems, 1999.Markdown
[Vapnik and Mukherjee. "Support Vector Method for Multivariate Density Estimation." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/vapnik1999neurips-support/)BibTeX
@inproceedings{vapnik1999neurips-support,
title = {{Support Vector Method for Multivariate Density Estimation}},
author = {Vapnik, Vladimir and Mukherjee, Sayan},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {659-665},
url = {https://mlanthology.org/neurips/1999/vapnik1999neurips-support/}
}