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COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Robust trainability of single neurons
Journal of Computer and System Sciences
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SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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The Journal of Machine Learning Research
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Kernel Methods for Pattern Analysis
Convex Optimization
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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The Journal of Machine Learning Research
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AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Maximal Discrepancy for Support Vector Machines
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PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Robust twin support vector machine for pattern classification
Pattern Recognition
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Advances in Fuzzy Systems
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International Journal of Intelligent Information and Database Systems
Robust novelty detection in the framework of a contamination neighbourhood
International Journal of Intelligent Information and Database Systems
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Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description
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The Journal of Machine Learning Research
Using robust dispersion estimation in support vector machines
Pattern Recognition
Classification and outlier detection based on topic based pattern synthesis
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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One of the well known risks of large margin training methods, such as boosting and support vector machines (SVMs), is their sensitivity to outliers. These risks are normally mitigated by using a soft margin criterion, such as hinge loss, to reduce outlier sensitivity. In this paper, we present a more direct approach that explicitly incorporates outlier suppression in the training process. In particular, we show how outlier detection can be encoded in the large margin training principle of support vector machines. By expressing a convex relaxation of the joint training problem as a semide finite program, one can use this approach to robustly train a support vector machine while suppressing outliers. We demonstrate that our approach can yield superior results to the standard soft margin approach in the presence of outliers.