NDLI: Soft Confidence-Weighted Learning

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ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 8

Issue 2, December 2016

Issue 1, October 2016

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Dystemo: Distant Supervision Method for Multi-Category Emotion Recognition in Tweets

Driving Profiles Computation and Monitoring for Car Insurance CRM

Soft Confidence-Weighted Learning

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 7

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 6

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ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 4

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 3

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 2

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 1

Soft Confidence-Weighted Learning

Content Provider	ACM Digital Library
Author	Wang, Jialei Zhao, Peilin Hoi, Steven C. H.
Copyright Year	2016
Description	Author Affiliation: Institute for Infocomm Research, A*STAR, Singapore(School of information systems, Singapore Management University, Singapore (Hoi, Steven C H; Department of computer science, University of Chicago, USA (Wang, Jialei); Zhao, Peilin))
Abstract	Online learning plays an important role in many big data mining problems because of its high efficiency and scalability. In the literature, many online learning algorithms using gradient information have been applied to solve online classification problems. Recently, more effective second-order algorithms have been proposed, where the correlation between the features is utilized to improve the learning efficiency. Among them, Confidence-Weighted (CW) learning algorithms are very effective, which assume that the classification model is drawn from a Gaussian distribution, which enables the model to be effectively updated with the second-order information of the data stream. Despite being studied actively, these CW algorithms cannot handle nonseparable datasets and noisy datasets very well. In this article, we propose a family of Soft Confidence-Weighted (SCW) learning algorithms for both binary classification and multiclass classification tasks, which is the first family of online classification algorithms that enjoys four salient properties simultaneously: (1) large margin training, (2) confidence weighting, (3) capability to handle nonseparable data, and (4) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the-art algorithms (including AROW, NAROW, and NHERD), we found that SCW in general achieves better or at least comparable predictive performance, but enjoys considerably better efficiency advantage (i.e., using a smaller number of updates and lower time cost). To facilitate future research, we release all the datasets and source code to the public at http://libol.stevenhoi.org/.
Starting Page	1
Ending Page	32
Page Count	32
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2932193
Volume Number	8
Issue Number	1
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-20
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Confidence weighted Binary classification Multiclass classification Second-order algorithms
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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