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ToolsJamil, 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.
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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: | Symplectic RT2 |
| Deposited On: | 05 Sep 2019 16:02 |
| Last Modified: | 14 Mar 2022 14:17 |
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