Exact Soft Confidence-Weighted Learning
This work addresses a limitation in confidence-weighted learning for online machine learning practitioners, offering an incremental improvement with enhanced properties.
The paper tackles the problem of handling non-separable cases in online learning by proposing a Soft Confidence-Weighted (SCW) scheme, which achieves better or comparable predictive accuracy and significant computational efficiency improvements compared to state-of-the-art algorithms.
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).