Heteroscedastic Relevance Vector Machine
Machine Learning
2013-01-11 v1 Machine Learning
Abstract
In this work we propose a heteroscedastic generalization to RVM, a fast Bayesian framework for regression, based on some recent similar works. We use variational approximation and expectation propagation to tackle the problem. The work is still under progress and we are examining the results and comparing with the previous works.
Cite
@article{arxiv.1301.2015,
title = {Heteroscedastic Relevance Vector Machine},
author = {Daniel Khashabi and Mojtaba Ziyadi and Feng Liang},
journal= {arXiv preprint arXiv:1301.2015},
year = {2013}
}