DUOL: A Double Updating Approach for Online Learning

In most online learning algorithms, the weights assigned to the misclassified examples (or support vectors) remain unchanged during the entire learning process. This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affe...

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Main Authors: ZHAO, Peilin, HOI, Steven C. H., JIN, Rong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2009
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Online Access:https://ink.library.smu.edu.sg/sis_research/2367
https://ink.library.smu.edu.sg/context/sis_research/article/3367/viewcontent/NIPS_DUOL_138CR.pdf
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spelling sg-smu-ink.sis_research-33672016-01-13T05:17:42Z DUOL: A Double Updating Approach for Online Learning ZHAO, Peilin HOI, Steven C. H. JIN, Rong In most online learning algorithms, the weights assigned to the misclassified examples (or support vectors) remain unchanged during the entire learning process. This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affect the weights for the existing support vectors. In this paper, we propose a new online learning method, termed Double Updating Online Learning, or DUOL for short. Instead of only assigning a fixed weight to the misclassified example received in current trial, the proposed online learning algorithm also tries to update the weight for one of the existing support vectors. We show that the mistake bound can be significantly improved by the proposed online learning method. Encouraging experimental results show that the proposed technique is in general considerably more effective than the state-of-the-art online learning algorithms 2009-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2367 https://ink.library.smu.edu.sg/context/sis_research/article/3367/viewcontent/NIPS_DUOL_138CR.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 Computer Sciences Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Sciences
Databases and Information Systems
spellingShingle Computer Sciences
Databases and Information Systems
ZHAO, Peilin
HOI, Steven C. H.
JIN, Rong
DUOL: A Double Updating Approach for Online Learning
description In most online learning algorithms, the weights assigned to the misclassified examples (or support vectors) remain unchanged during the entire learning process. This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affect the weights for the existing support vectors. In this paper, we propose a new online learning method, termed Double Updating Online Learning, or DUOL for short. Instead of only assigning a fixed weight to the misclassified example received in current trial, the proposed online learning algorithm also tries to update the weight for one of the existing support vectors. We show that the mistake bound can be significantly improved by the proposed online learning method. Encouraging experimental results show that the proposed technique is in general considerably more effective than the state-of-the-art online learning algorithms
format text
author ZHAO, Peilin
HOI, Steven C. H.
JIN, Rong
author_facet ZHAO, Peilin
HOI, Steven C. H.
JIN, Rong
author_sort ZHAO, Peilin
title DUOL: A Double Updating Approach for Online Learning
title_short DUOL: A Double Updating Approach for Online Learning
title_full DUOL: A Double Updating Approach for Online Learning
title_fullStr DUOL: A Double Updating Approach for Online Learning
title_full_unstemmed DUOL: A Double Updating Approach for Online Learning
title_sort duol: a double updating approach for online learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2009
url https://ink.library.smu.edu.sg/sis_research/2367
https://ink.library.smu.edu.sg/context/sis_research/article/3367/viewcontent/NIPS_DUOL_138CR.pdf
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