@inproceedings{Bodesheim12:DOC,
    type = {inproceedings},
    key = {Bodesheim12:DOC},
    title = {Divergence-Based One-Class Classification Using Gaussian Processes},
    author = {Paul Bodesheim and Erik Rodner and Alexander Freytag and Joachim Denzler},
    booktitle = {British Machine Vision Conference (BMVC)},
    year = {2012},
    pages = {50.1--50.11},
    abstract = {We present an information theoretic framework for one-class classification, which allows for deriving several new novelty scores. With these scores, we are able to rank samples according to their novelty and to detect outliers not belonging to a learnt data distribution. The key idea of our approach is to measure the impact of a test sample on the previously learnt model. This is carried out in a probabilistic manner using Jensen-Shannon divergence and reclassification results derived from the Gaussian process regression framework. Our method is evaluated using well-known machine learning datasets as well as large-scale image categorisation experiments showing its ability to achieve state-of-the-art performance.},
    doi = {10.5244/C.26.50},
    groups = {lifelonglearning,noveltydetection,gaussianprocesses},
    url = {https://dx.doi.org/10.5244/C.26.50},
}