Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2009
Abstract
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
Discipline
Computer Sciences | Databases and Information Systems
Publication
Advances in Neural Information Processing Systems: 23rd Annual Conference on Neural Information Processing Systems 2009, December 7-10, 2009, Vancouver, BC, Canada
Volume
22
ISBN
9781615679119
Publisher
Neural Information Processing Systems
City or Country
La Jolla, CA
Citation
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,.
Available at: https://ink.library.smu.edu.sg/sis_research/2367
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://papers.nips.cc/paper/3787-duol-a-double-updating-approach-for-online-learning