In sequence labeling problems, the objective functions of most learning algorithms are usually inconsistent with evaluation measures, such as Hamming loss. In this paper, we propose an online learning algorithm that addresses the problem of labelwise margin maximization for sequence labeling. We decompose the sequence margin to per-label margins and maximize these per-label margins individually, which can result to minimize the Hamming loss of sequence. We compare our algorithm with three state-of-art methods on three tasks, and the experimental results show our algorithm outperforms the others. © 2011 Springer-Verlag.
CITATION STYLE
Gao, W., Qiu, X., & Huang, X. (2011). Labelwise margin maximization for sequence labeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6608 LNCS, pp. 121–132). https://doi.org/10.1007/978-3-642-19400-9_10
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