NDLI: Training structural svms with kernels using sampled cuts

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Training structural svms with kernels using sampled cuts

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Training structural svms with kernels using sampled cuts

Content Provider	ACM Digital Library
Author	Yu, Chun-Nam John Joachims, Thorsten
Abstract	Discriminative training for structured outputs has found increasing applications in areas such as natural language processing, bioinformatics, information retrieval, and computer vision. Focusing on large-margin methods, the most general (in terms of loss function and model structure) training algorithms known to date are based on cutting-plane approaches. While these algorithms are very efficient for linear models, their training complexity becomes quadratic in the number of examples when kernels are used. To overcome this bottleneck, we propose new training algorithms that use approximate cutting planes and random sampling to enable efficient training with kernels. We prove that these algorithms have improved time complexity while providing approximation guarantees. In empirical evaluations, our algorithms produced solutions with training and test error rates close to those of exact solvers. Even on binary classification problems where highly optimized conventional training methods exist (e.g. SVM-light), our methods are about an order of magnitude faster than conventional training methods on large datasets, while remaining competitive in speed on datasets of medium size.
Starting Page	794
Ending Page	802
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1401985
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Kernels Support vector machines Large-scale problems
Content Type	Text
Resource Type	Article

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