Covariance Kernels from Bayesian Generative Models
Abstract
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task data using Bayesian techniques. We describe an implementation of this frame(cid:173) work which uses variational Bayesian mixtures of factor analyzers in order to attack classification problems in high-dimensional spaces where labeled data is sparse, but unlabeled data is abundant.
Cite
Text
Seeger. "Covariance Kernels from Bayesian Generative Models." Neural Information Processing Systems, 2001.Markdown
[Seeger. "Covariance Kernels from Bayesian Generative Models." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/seeger2001neurips-covariance/)BibTeX
@inproceedings{seeger2001neurips-covariance,
title = {{Covariance Kernels from Bayesian Generative Models}},
author = {Seeger, Matthias},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {905-912},
url = {https://mlanthology.org/neurips/2001/seeger2001neurips-covariance/}
}