Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Data Description
Machine Learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
The Journal of Machine Learning Research
Machine learning approaches to network anomaly detection
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
Novelty detection in blind steganalysis
Proceedings of the 10th ACM workshop on Multimedia and security
Data stream anomaly detection through principal subspace tracking
Proceedings of the 2010 ACM Symposium on Applied Computing
Monitoring nonlinear profiles using support vector machines
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Delimiting imprecise regions with georeferenced photos and land coverage data
W2GIS'11 Proceedings of the 10th international conference on Web and wireless geographical information systems
Local linear approximation for kernel methods: the railway kernel
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
A new distance for probability measures based on the estimation of level sets
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Review: A review of novelty detection
Signal Processing
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In this paper, we investigate the problem of estimating high-density regions from univariate or multivariate data samples. We estimate minimum volume sets, whose probability is specified in advance, known in the literature as density contour clusters. This problem is strongly related to One-Class Support Vector Machines (OCSVM). We propose a new method to solve this problem, the One-Class Neighbor Machine (OCNM) and we show its properties. In particular, the OCNM solution asymptotically converges to the exact minimum volume set prespecified. Finally, numerical results illustrating the advantage of the new method are shown.