Making large-scale support vector machine learning practical
Advances in kernel methods
The Journal of Machine Learning Research
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Sparseness of support vector machines
The Journal of Machine Learning Research
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The Proposition Bank: An Annotated Corpus of Semantic Roles
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Training linear SVMs in linear time
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Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
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Optimized cutting plane algorithm for support vector machines
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Training structural svms with kernels using sampled cuts
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse kernel SVMs via cutting-plane training
Machine Learning
Hash Kernels for Structured Data
The Journal of Machine Learning Research
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
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ACL '12 Tutorial Abstracts of ACL 2012
Structured image segmentation using kernelized features
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Fast linearization of tree kernels over large-scale data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper, we propose three important enhancements of the approximate cutting plane algorithm (CPA) to train Support Vector Machines with structural kernels: (i) we exploit a compact yet exact representation of cutting plane models using directed acyclic graphs to speed up both training and classification, (ii) we provide a parallel implementation, which makes the training scale almost linearly with the number of CPUs, and (iii) we propose an alternative sampling strategy to handle class-imbalanced problem and show that theoretical convergence bounds are preserved. The experimental evaluations on three diverse datasets demonstrate the soundness of our approach and the possibility to carry out fast learning and classification with structural kernes.