@inproceedings{929072970108467b8432cd3d14ceef83,
title = "A sparse L2-regularized support vector machines for large-scale natural language learning",
abstract = "Linear support vector machines (SVMs) have become one of the most prominent classification algorithms for many natural language learning problems such as sequential labeling tasks. Even though the L2-regularized SVMs yields slightly more superior accuracy than L1-SVM, it produces too much near but non zero feature weights. In this paper, we present a cutting-weight algorithm to guide the optimization process of L 2-SVM into sparse solution. To verify the proposed method, we conduct the experiments with three well-known sequential labeling tasks and one dependency parsing task. The result shows that our method achieved at least 400% feature parameter reduction rates in comparison to the original L2-SVM, with almost no change in accuracy and training times. In terms of run time efficiency, our method is faster than the original L2-regularized SVMs at least 20% in all tasks.",
keywords = "L-regularization, machine learning, part-of-speech tagging, support vector machines",
author = "Wu, {Yu Chieh} and Lee, {Yue Shi} and Yang, {Jie Chi} and Yen, {Show Jane}",
year = "2010",
doi = "10.1007/978-3-642-17187-1_33",
language = "???core.languages.en_GB???",
isbn = "3642171869",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "340--349",
booktitle = "Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings",
note = "6th Asia Information Retrieval Societies Conference, AIRS 2010 ; Conference date: 01-12-2010 Through 03-12-2010",
}