Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A D. C. Optimization Algorithm for Solving the Trust-Region Subproblem
SIAM Journal on Optimization
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pareto optimal linear classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Journal of Machine Learning Research
Considering Cost Asymmetry in Learning Classifiers
The Journal of Machine Learning Research
General solution and learning method for binary classification with performance constraints
Pattern Recognition Letters
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
The Journal of Machine Learning Research
The entire quantile path of a risk-agnostic SVM classifier
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Imbalanced learning with a biased minimax probability machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Neyman-Pearson approach to statistical learning
IEEE Transactions on Information Theory
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We describe and evaluate two algorithms for Neyman-Pearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch algorithm based on DC programming and stochastic gradient method well suited for large-scale datasets. Empirical evidences illustrate the potential of the proposed methods.