Publication Type

Conference Proceeding Article

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

Research Areas

Data Management and Analytics

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://papers.nips.cc/paper/3787-duol-a-double-updating-approach-for-online-learning

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