Batch and on-line parameter estimation of Gaussian mixtures based on the joint entropy
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Interpreting Kullback-Leibler divergence with the Neyman-Pearson lemma
Journal of Multivariate Analysis - Special issue dedicated to Professor Yasunori Fujikoshi
Neural tree density estimation for novelty detection
IEEE Transactions on Neural Networks
Gradual land cover change detection based on multitemporal fraction images
Pattern Recognition
One-class classification with Gaussian processes
Pattern Recognition
An information theoretic sparse kernel algorithm for online learning
Expert Systems with Applications: An International Journal
Review: A review of novelty detection
Signal Processing
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We present a novel approach to online change detection problems when the training sample size is small. The proposed approach is based on estimating the expected information content of a new data point and allows an accurate control of the false positive rate even for small data sets. In the case of the Gaussian distribution, our approach is analytically tractable and closely related to classical statistical tests. We then propose an approximation scheme to extend our approach to the case of the mixture of Gaussians. We evaluate extensively our approach on synthetic data and on three real benchmark data sets. The experimental validation shows that our method maintains a good overall accuracy, but significantly improves the control over the false positive rate.