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Competitive Regularised Regression.

Jamil, W. and Bouchachia, A., 2020. Competitive Regularised Regression. Neurocomputing, 390 (May), 374-383.

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CompetitiveRegRegression.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1016/j.neucom.2019.08.094


Regularised regression uses sparsity and variance to reduce the complexity and over-fitting of a regression model. The present paper introduces two novel regularised linear regression algorithms: Competitive Iterative Ridge Regression (CIRR) and Online Shrinkage via Limit of Gibbs Sampler (OSLOG) for fast and reliable prediction on "Big Data" without making distributional assumption on the data. We use the technique of competitive analysis to design them and show their strong theoretical guarantee. Furthermore, we compare their performance against some neoteric regularised regression methods such as On-line Ridge Regression (ORR) and the Aggregating Algorithm for Regression (AAR). The comparison of the algorithms is done theoretically, focusing on the guarantee on the performance on cumulative loss, and empirically to show the advantages of CIRR and OSLOG.

Item Type:Article
Uncontrolled Keywords:Regression; Regularisation; Online learning; Competitive analysis
Group:Faculty of Science & Technology
ID Code:32713
Deposited By: Symplectic RT2
Deposited On:05 Sep 2019 08:48
Last Modified:14 Mar 2022 14:17


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