@inproceedings{freytag2012beyond,
    type = {inproceedings},
    key = {freytag2012beyond},
    title = {Beyond Classification - Large-scale Gaussian Process Inference and Uncertainty Prediction},
    author = {Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler},
    booktitle = {Big Data Meets Computer Vision: First International Workshop on Large Scale Visual Recognition and Retrieval (NIPS-WS)},
    year = {2012},
    note = {This workshop article is a short version of our ACCV 2012 paper.},
    abstract = {Due to the massive (labeled) data available on the web, a tremendous interest in large-scale machine learning methods has emerged in the last years. Whereas, most of the work done in this new area of research focused on fast and efficient classification algorithms, we show in this paper how other aspects of learning can also be covered using massive datasets. The paper briefly presents techniques allowing for utilizing the full posterior obtained from Gaussian process regression (predictive mean and variance) with tens of thousands of data points and without relying on sparse approximation approaches. Experiments are done for active learning and one-class classification showing the benefits in large-scale settings.},
    groups = {gaussianprocesses,largescale},
    keywords = {gaussian processes, histogram intersection kernels, large-scale classification, uncertainty prediction},
}