Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2012

Abstract

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).

Keywords

C-W algorithm, Learning methods, Online learning scheme, Predictive accuracy, State-of-the-art algorithms

Discipline

Computer Sciences | Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the Twenty-Ninth International Conference on Machine Learning: ICML 2012, June 26 - July 1, Edinburgh, Scotland

First Page

121

Last Page

128

ISBN

9781450312851

Publisher

International Machine Learning Society

City or Country

Madison, WI

Copyright Owner and License

Authors

Additional URL

https://icml.cc/2012/papers/86.pdf

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