Skip to main content

Online Bayesian Shrinkage Regression.

Jamil, W. and Bouchachia, A., 2019. Online Bayesian Shrinkage Regression. In: The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 24--26 April 2019, Bruges, Belgium.

Full text available as:

[img]
Preview
PDF
ESANN2019-105.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.

1MB

Official URL: https://www.elen.ucl.ac.be/esann/

Abstract

The present work introduces a new online regression method that extends the Shrinkage via Limit of Gibbs sampler (SLOG) in the context of online learning. In particular, we theoretically demonstrate that the proposed Online SLOG (OSLOG) is derived using the Bayesian framework without resorting to the Gibbs sampler. We also state the performance guarantee of OSLOG.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:32716
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:05 Sep 2019 16:02
Last Modified:25 Oct 2019 11:03

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -