Surface street traffic estimation
Proceedings of the 5th international conference on Mobile systems, applications and services
The Journal of Machine Learning Research
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
The Journal of Machine Learning Research
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Cluster analysis of massive datasets in astronomy
Statistics and Computing
Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The Journal of Machine Learning Research
Novelty detection in blind steganalysis
Proceedings of the 10th ACM workshop on Multimedia and security
An evaluation of dimension reduction techniques for one-class classification
Artificial Intelligence Review
Estimating Anomality of the Video Sequences for Surveillance Using 1-Class SVM
IEICE - Transactions on Information and Systems
ACM Computing Surveys (CSUR)
Foundations of r-contiguous matching in negative selection for anomaly detection
Natural Computing: an international journal
A Fast Feature-Based Method to Detect Unusual Patterns in Multidimensional Datasets
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Online anomaly detection using KDE
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Complexity-penalized estimation of minimum volume sets for dependent data
Journal of Multivariate Analysis
Density-based similarity measures for content based search
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Semi-Supervised Novelty Detection
The Journal of Machine Learning Research
Feature selection for SVM-based vascular anomaly detection
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Learning from only positive and unlabeled data to detect lesions in vascular CT images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Robust novelty detection in the framework of a contamination neighbourhood
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
Security analysis of online centroid anomaly detection
The Journal of Machine Learning Research
Confidence regions for level sets
Journal of Multivariate Analysis
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One way to describe anomalies is by saying that anomalies are not concentrated. This leads to the problem of finding level sets for the data generating density. We interpret this learning problem as a binary classification problem and compare the corresponding classification risk with the standard performance measure for the density level problem. In particular it turns out that the empirical classification risk can serve as an empirical performance measure for the anomaly detection problem. This allows us to compare different anomaly detection algorithms empirically, i.e. with the help of a test set. Furthermore, by the above interpretation we can give a strong justification for the well-known heuristic of artificially sampling "labeled" samples, provided that the sampling plan is well chosen. In particular this enables us to propose a support vector machine (SVM) for anomaly detection for which we can easily establish universal consistency. Finally, we report some experiments which compare our SVM to other commonly used methods including the standard one-class SVM.