Soft confidence-weighted learning

Online learning plays an important role in many big datamining problems because of its high efficiency and scalability. In theliterature, many online learning algorithms using gradient information havebeen applied to solve online classification problems. Recently, more effectivesecond-order algorith...

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Main Authors: WANG, Jialei, ZHAO, Peilin, HOI, Steven C. H.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/3418
https://ink.library.smu.edu.sg/context/sis_research/article/4419/viewcontent/SoftConfidenceWeightedLearning_2016.pdf
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spelling sg-smu-ink.sis_research-44192020-04-02T06:18:54Z Soft confidence-weighted learning WANG, Jialei ZHAO, Peilin HOI, Steven C. H., Online learning plays an important role in many big datamining problems because of its high efficiency and scalability. In theliterature, many online learning algorithms using gradient information havebeen applied to solve online classification problems. Recently, more effectivesecond-order algorithms have been proposed, where the correlation between thefeatures is utilized to improve the learning efficiency. Among them,Confidence-Weighted (CW) learning algorithms are very effective, which assumethat the classification model is drawn from a Gaussian distribution, whichenables the model to be effectively updated with the second-order informationof the data stream. Despite being studied actively, these CW algorithms cannothandle nonseparable datasets and noisy datasets very well. In this article, wepropose a family of Soft Confidence-Weighted (SCW) learning algorithms for bothbinary classification and multiclass classification tasks, which is the firstfamily of online classification algorithms that enjoys four salient propertiessimultaneously: (1) large margin training, (2) confidence weighting, (3)capability to handle nonseparable data, and (4) adaptive margin. Ourexperimental results show that the proposed SCW algorithms significantlyoutperform the original CW algorithm. When comparing with a variety ofstate-of-the-art algorithms (including AROW, NAROW, and NHERD), we found thatSCW in general achieves better or at least comparable predictive performance,but enjoys considerably better efficiency advantage (i.e., using a smallernumber of updates and lower time cost). To facilitate future research, werelease all the datasets and source code to the public athttp://libol.stevenhoi.org/. 2016-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3418 info:doi/10.1145/2932193 https://ink.library.smu.edu.sg/context/sis_research/article/4419/viewcontent/SoftConfidenceWeightedLearning_2016.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University machine learning online learning Computer Sciences Databases and Information Systems Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic machine learning
online learning
Computer Sciences
Databases and Information Systems
Theory and Algorithms
spellingShingle machine learning
online learning
Computer Sciences
Databases and Information Systems
Theory and Algorithms
WANG, Jialei
ZHAO, Peilin
HOI, Steven C. H.,
Soft confidence-weighted learning
description Online learning plays an important role in many big datamining problems because of its high efficiency and scalability. In theliterature, many online learning algorithms using gradient information havebeen applied to solve online classification problems. Recently, more effectivesecond-order algorithms have been proposed, where the correlation between thefeatures is utilized to improve the learning efficiency. Among them,Confidence-Weighted (CW) learning algorithms are very effective, which assumethat the classification model is drawn from a Gaussian distribution, whichenables the model to be effectively updated with the second-order informationof the data stream. Despite being studied actively, these CW algorithms cannothandle nonseparable datasets and noisy datasets very well. In this article, wepropose a family of Soft Confidence-Weighted (SCW) learning algorithms for bothbinary classification and multiclass classification tasks, which is the firstfamily of online classification algorithms that enjoys four salient propertiessimultaneously: (1) large margin training, (2) confidence weighting, (3)capability to handle nonseparable data, and (4) adaptive margin. Ourexperimental results show that the proposed SCW algorithms significantlyoutperform the original CW algorithm. When comparing with a variety ofstate-of-the-art algorithms (including AROW, NAROW, and NHERD), we found thatSCW in general achieves better or at least comparable predictive performance,but enjoys considerably better efficiency advantage (i.e., using a smallernumber of updates and lower time cost). To facilitate future research, werelease all the datasets and source code to the public athttp://libol.stevenhoi.org/.
format text
author WANG, Jialei
ZHAO, Peilin
HOI, Steven C. H.,
author_facet WANG, Jialei
ZHAO, Peilin
HOI, Steven C. H.,
author_sort WANG, Jialei
title Soft confidence-weighted learning
title_short Soft confidence-weighted learning
title_full Soft confidence-weighted learning
title_fullStr Soft confidence-weighted learning
title_full_unstemmed Soft confidence-weighted learning
title_sort soft confidence-weighted learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/3418
https://ink.library.smu.edu.sg/context/sis_research/article/4419/viewcontent/SoftConfidenceWeightedLearning_2016.pdf
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