常用被引用文献 MIRA CRF

最新推荐文章于 2022-04-04 23:45:43 发布

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最新推荐文章于 2022-04-04 23:45:43 发布

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分类专栏： NLP 文章标签： training parsing random algorithm tree 算法

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本文介绍了MIRA（Margin Infused Relaxed Algorithm）和CRF（Conditional Random Fields）两种机器学习算法。MIRA是一种在线学习算法，在分类、排序、预测等领域表现出色；CRF则是一种用于序列标注问题的概率模型，广泛应用于自然语言处理任务中。

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MIRA （Margin Infused Relaxed Algorithm ）一种超保守在线算法, 在分类、排序、预测等应用领域取得不错成绩

理论

Koby Crammer.
2004. Online Learning of Com-plex Categorial Problems. Hebrew Univeristy of Jerusalem, PhD Thesis
2005. Ryan McDonald, and Fernando Pereira.Scalable large-margin online learning for structured classification.In NIPS Workshop on Learning With Structured Outputs
2006.Dekel O, Keshet J, Shalev-Shwartz S, SingerY. Online passive-aggressive algorithms.Journal of Machine Learning Research, 7: 551¡585
Ryan McDonald
2005.Crammer K, Pereira F. Online large-margin training of dependency parsers. In: Proceedings of the 43rd Annual Meeting on the Association for Computational Lin-guistics. Ann Arbor, USA: ACL, . 91¡98
2005. Femando Pereira, Kiril Ribarow, and Jan Hajic. Non-projective dependency parsing using spanning tree algorithms. In Proceedings of HLT/EMNLP, pages 523–530.
2006.Discriminative Training and Spanning Tree Algorithms for Dependency Parsing. University of Pennsylvania, PhD Thesis.

使用

2009.An Error-Driven Word-Character Hybrid Model for Joint Chinese Word Segmentation and POS Tagging

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CRF（Conditional random fields）条件随机场，2001开始用于序列标注问题

理论

John Lafferty
2001. Andrew McCallum, and Fernando Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. InProceedings of ICML, pages 282–289

使用

2012. Fast Online Training with Frequency-Adaptive Learning Rates for Chinese Word Segmentation and New

未完待续……