Competitive regularised regression
Authors: Jamil, W. and Bouchachia, A.
Journal: Neurocomputing
Volume: 390
Pages: 374-383
eISSN: 1872-8286
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.08.094
Abstract: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 Online 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.
https://eprints.bournemouth.ac.uk/32713/
Source: Scopus
Competitive regularised regression
Authors: Jamil, W. and Bouchachia, A.
Journal: NEUROCOMPUTING
Volume: 390
Pages: 374-383
eISSN: 1872-8286
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.08.094
https://eprints.bournemouth.ac.uk/32713/
Source: Web of Science (Lite)
Competitive Regularised Regression
Authors: Jamil, W. and Bouchachia, A.
Journal: Neurocomputing
Publisher: Elsevier
ISSN: 0925-2312
Abstract: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.
https://eprints.bournemouth.ac.uk/32713/
Source: Manual
Competitive Regularised Regression
Authors: Jamil, W. and Bouchachia, A.
Journal: Neurocomputing
Volume: 390
Issue: May
Pages: 374-383
ISSN: 0925-2312
Abstract: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.
https://eprints.bournemouth.ac.uk/32713/
Source: BURO EPrints