Elements of information theory
Elements of information theory
Learning distributions by their density levels: a paradigm for learning without a teacher
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Model Selection and Error Estimation
Machine Learning
Complexity penalized support estimation
Journal of Multivariate Analysis
A Classification Framework for Anomaly Detection
The Journal of Machine Learning Research
Tutorial on Practical Prediction Theory for Classification
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Estimation of High-Density Regions Using One-Class Neighbor Machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Concept learning using complexity regularization
IEEE Transactions on Information Theory
Rademacher penalties and structural risk minimization
IEEE Transactions on Information Theory
A Neyman-Pearson approach to statistical learning
IEEE Transactions on Information Theory
Minimax-optimal classification with dyadic decision trees
IEEE Transactions on Information Theory
Nonparametric estimation via empirical risk minimization
IEEE Transactions on Information Theory
Cluster analysis of massive datasets in astronomy
Statistics and Computing
Machine learning approaches to network anomaly detection
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
The Journal of Machine Learning Research
An evaluation of dimension reduction techniques for one-class classification
Artificial Intelligence Review
Nested support vector machines
IEEE Transactions on Signal Processing
Outlier detection via localized p-value estimation
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Adaptive estimation of the optimal ROC curve and a bipartite ranking algorithm
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Complexity-penalized estimation of minimum volume sets for dependent data
Journal of Multivariate Analysis
A Computable Plug-In Estimator of Minimum Volume Sets for Novelty Detection
Operations Research
Semi-Supervised Novelty Detection
The Journal of Machine Learning Research
Selecting training points for one-class support vector machines
Pattern Recognition Letters
A novel parameter refinement approach to one class support vector machine
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Deterministic annealing multi-sphere support vector data description
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Confidence regions for level sets
Journal of Multivariate Analysis
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Given a probability measure P and a reference measure μ, one is often interested in the minimum μ-measure set with P-measure at least α. Minimum volume sets of this type summarize the regions of greatest probability mass of P, and are useful for detecting anomalies and constructing confidence regions. This paper addresses the problem of estimating minimum volume sets based on independent samples distributed according to P. Other than these samples, no other information is available regarding P, but the reference measure μ is assumed to be known. We introduce rules for estimating minimum volume sets that parallel the empirical risk minimization and structural risk minimization principles in classification. As in classification, we show that the performances of our estimators are controlled by the rate of uniform convergence of empirical to true probabilities over the class from which the estimator is drawn. Thus we obtain finite sample size performance bounds in terms of VC dimension and related quantities. We also demonstrate strong universal consistency, an oracle inequality, and rates of convergence. The proposed estimators are illustrated with histogram and decision tree set estimation rules.