Online Bayesian shrinkage regression
Authors: Jamil, W. and Bouchachia, A.
Journal: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages: 431-436
ISBN: 9782875870650
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.
https://eprints.bournemouth.ac.uk/32716/
Source: Scopus
Online Bayesian shrinkage regression
Authors: Jamil, W. and Bouchachia, A.
Journal: Neural Computing and Applications
Volume: 32
Issue: 23
Pages: 17759-17767
eISSN: 1433-3058
ISSN: 0941-0643
DOI: 10.1007/s00521-020-04947-y
Abstract:The present work introduces an original and new online regression method that extends the shrinkage via limit of Gibbs sampler (SLOG) in the context of online learning. In particular, we theoretically show how the proposed online SLOG (OSLOG) is obtained using the Bayesian framework without resorting to the Gibbs sampler or considering a hierarchical representation. Moreover, in order to define the performance guarantee of OSLOG, we derive an upper bound on the cumulative squared loss. It is the only online regression algorithm with sparsity that gives logarithmic regret. Furthermore, we do an empirical comparison with two state-of-the-art algorithms to illustrate the performance of OSLOG relying on three aspects: normality, sparsity and multicollinearity showing an excellent achievement of trade-off between these properties.
https://eprints.bournemouth.ac.uk/32716/
Source: Scopus
Online Bayesian shrinkage regression
Authors: Jamil, W. and Bouchachia, A.
Journal: NEURAL COMPUTING & APPLICATIONS
Volume: 32
Issue: 23
Pages: 17759-17767
eISSN: 1433-3058
ISSN: 0941-0643
DOI: 10.1007/s00521-020-04947-y
https://eprints.bournemouth.ac.uk/32716/
Source: Web of Science (Lite)
Online Bayesian Shrinkage Regression
Authors: Jamil, W. and Bouchachia, A.
Conference: ESANN 2019: The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Dates: 24-26 April 2019
https://eprints.bournemouth.ac.uk/32716/
Source: Manual
Online Bayesian Shrinkage Regression
Authors: Bouchachia, A.
Conference: 2019 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Dates: 24-26 April 2019
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.
https://eprints.bournemouth.ac.uk/32716/
Source: Manual
Online Bayesian Shrinkage Regression
Authors: Jamil, W. and Bouchachia, A.
Conference: The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
ISBN: 978-287-587-065-0
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.
https://eprints.bournemouth.ac.uk/32716/
https://www.elen.ucl.ac.be/esann/
Source: BURO EPrints