The nature of statistical learning theory
The nature of statistical learning theory
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
A robust multilingual portable phrase chunking system
Expert Systems with Applications: An International Journal
splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Polynomial to linear: efficient classification with conjunctive features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Kernel slicing: scalable online training with conjunctive features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Shell fitting space for classification
Expert Systems with Applications: An International Journal
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Kernel methods such as support vector machines (SVMs) have attracted a great deal of popularity in the machine learning and natural language processing (NLP) communities. Polynomial kernel SVMs showed very competitive accuracy in many NLP problems, like part-of-speech tagging and chunking. However, these methods are usually too inefficient to be applied to large dataset and real time purpose. In this paper, we propose an approximate method to analogy polynomial kernel with efficient data mining approaches. To prevent exponential-scaled testing time complexity, we also present a new method for speeding up SVM classifying which does independent to the polynomial degree d. The experimental results showed that our method is 16.94 and 450 times faster than traditional polynomial kernel in terms of training and testing respectively.