The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Alternate Objective Function for Markovian Fields
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Chinese segmentation and new word detection using conditional random fields
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Piecewise pseudolikelihood for efficient training of conditional random fields
Proceedings of the 24th international conference on Machine learning
Minimum tag error for discriminative training of conditional random fields
Information Sciences: an International Journal
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Piecewise training for structured prediction
Machine Learning
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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.