Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Convex Optimization
Nightmare at test time: robust learning by feature deletion
ICML '06 Proceedings of the 23rd international conference on Machine learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
On the generalization ability of on-line learning algorithms
IEEE Transactions on Information Theory
Good learners for evil teachers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Stackelberg games for adversarial prediction problems
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Adversarial support vector machine learning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Static prediction games for adversarial learning problems
The Journal of Machine Learning Research
Security analysis of online centroid anomaly detection
The Journal of Machine Learning Research
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After a classifier is trained using a machine learning algorithm and put to use in a real world system, it often faces noise which did not appear in the training data. Particularly, some subset of features may be missing or may become corrupted. We present two novel machine learning techniques that are robust to this type of classification-time noise. First, we solve an approximation to the learning problem using linear programming. We analyze the tightness of our approximation and prove statistical risk bounds for this approach. Second, we define the online-learning variant of our problem, address this variant using a modified Perceptron, and obtain a statistical learning algorithm using an online-to-batch technique. We conclude with a set of experiments that demonstrate the effectiveness of our algorithms.