@inproceedings{1213e8a50b3f474cb65160ffbb8d309c,
title = "Chinese spam filter based on relaxed online support vector machine",
abstract = "For spam filtering, a classical online learning problem, Online SVM is less applicable for its low efficiency in dealing with the ever increasing training sample set. For this purpose, the Relaxed Online SVM (ROSVM) model by relaxing its constraints can significantly improve training speed with little cost on filtering performance. In this paper, we applied this model to Chinese spam filtering and experimental results have outperformed the best in the TREC 2006 Chinese spam filtering track. Our filter is further validated by the SEWM 2010 dataset, ranking the best according to 1-ROCA\% in the delayed feedback task and the active learning task.",
keywords = "Chinese spam filtering, Online learning, Relaxed online SVM",
author = "Yong Han and Xiaoning He and Muyun Yang and Haoliang Qi and Chao Song",
year = "2010",
doi = "10.1109/IALP.2010.90",
language = "英语",
isbn = "9780769542881",
series = "Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010",
pages = "185--188",
booktitle = "Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010",
note = "2010 International Conference on Asian Language Processing, IALP 2010 ; Conference date: 28-12-2010 Through 30-12-2010",
}