Conference Proceeding Article
In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) 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 generally achieves better or at least comparable predictive accuracy, but enjoys significant advantage of computational efficiency (i.e., smaller number of updates and lower time cost).
Computer Sciences | Databases and Information Systems
Data Management and Analytics
Proceedings of the Twenty-Ninth International Conference on Machine Learning: June 26 - July 1, Edinburgh, Scotland
International Machine Learning Society
City or Country
WANG, Jialei and HOI, Steven C. H..
Exact Soft Confidence-Weighted Learning. (2012). Proceedings of the Twenty-Ninth International Conference on Machine Learning: June 26 - July 1, Edinburgh, Scotland. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2341
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