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The art of computer programming, volume 3: (2nd ed.) sorting and searching
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Neural Computation
Foundations of statistical natural language processing
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Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Kernel PCA and de-noising in feature spaces
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The 1999 DARPA off-line intrusion detection evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Introduction to Automata Theory, Languages and Computability
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Applications of Data Mining in Computer Security
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ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A high-level programming environment for packet trace anonymization and transformation
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
A Maximum-Entropy-Inspired Parser
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Kernel Methods for Pattern Analysis
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WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
binpac: a yacc for writing application protocol parsers
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ACM SIGIR Forum
Convolution kernels with feature selection for natural language processing tasks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Fast and effective kernels for relational learning from texts
Proceedings of the 24th international conference on Machine learning
On Relevant Dimensions in Kernel Feature Spaces
The Journal of Machine Learning Research
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
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An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Large-scale support vector learning with structural kernels
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Sentimental Spidering: Leveraging Opinion Information in Focused Crawlers
ACM Transactions on Information Systems (TOIS)
Similarity measures for sequential data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Convolution kernels for trees provide simple means for learning with tree-structured data. The computation time of tree kernels is quadratic in the size of the trees, since all pairs of nodes need to be compared. Thus, large parse trees, obtained from HTML documents or structured network data, render convolution kernels inapplicable. In this article, we propose an effective approximation technique for parse tree kernels. The approximate tree kernels (ATKs) limit kernel computation to a sparse subset of relevant subtrees and discard redundant structures, such that training and testing of kernel-based learning methods are significantly accelerated. We devise linear programming approaches for identifying such subsets for supervised and unsupervised learning tasks, respectively. Empirically, the approximate tree kernels attain run-time improvements up to three orders of magnitude while preserving the predictive accuracy of regular tree kernels. For unsupervised tasks, the approximate tree kernels even lead to more accurate predictions by identifying relevant dimensions in feature space.