Conference Proceeding Article
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
Computer Sciences | Databases and Information Systems
Data Management and Analytics
Advances in Neural Information Processing Systems: 23rd Annual Conference on Neural Information Processing Systems 2009, December 7-10, 2009, Vancouver, BC, Canada
Neural Information Processing Systems
City or Country
La Jolla, CA
ZHAO, Peilin; HOI, Steven C. H.; and JIN, Rong.
DUOL: A Double Updating Approach for Online Learning. (2009). Advances in Neural Information Processing Systems: 23rd Annual Conference on Neural Information Processing Systems 2009, December 7-10, 2009, Vancouver, BC, Canada. 22,. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2367
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