A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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Combining support vector and mathematical programming methods for classification
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Generalisation Error Bounds for Sparse Linear Classifiers
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
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The Journal of Machine Learning Research
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Learning with Decision Lists of Data-Dependent Features
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Rademacher averages and phase transitions in Glivenko-Cantelli classes
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A selective sampling strategy for label ranking
ECML'06 Proceedings of the 17th European conference on Machine Learning
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We propose a new learning algorithm for the set covering machine and a tight data-compression risk bound that the learner can use for choosing the appropriate tradeoff between the sparsity of a classifier and the magnitude of its separating margin.