Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting the Number of Clusters Using a Support Vector Machine Approach
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Estimating the Support of a High-Dimensional Distribution
Neural Computation
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In this work, the problem of estimating high density regions from univariate or multivariate data samples is studied. To be more precise, we estimate minimum volume sets whose probability is specified in advance. This problem arises in outlier detection and cluster analysis, and is strongly related to One-Class Support Vector Machines (SVM). In this paper we propose a new simpler method to solve this problem. We show its properties and introduce a new class of kernels, relating the proposed method to One-Class SVMs.