@inproceedings{freytag2012rapid,
    type = {inproceedings},
    key = {freytag2012rapid},
    title = {Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels},
    author = {Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler},
    booktitle = {Asian Conference on Computer Vision (ACCV)},
    year = {2012},
    note = {Best Paper Honorable Mention Award},
    pages = {511-524},
    abstract = {An important advantage of Gaussian processes is the ability to directly estimate classification uncertainties in a Bayesian manner. In this paper, we develop techniques that allow for estimating these uncertainties with a runtime linear or even constant with respect to the number of training examples. Our approach makes use of all training data without any sparse approximation technique while needing only a linear amount of memory. To incorporate new information over time, we further derive online learning methods leading to significant speed-ups and allowing for hyperparameter optimization on-the-fly. We conduct several experiments on public image datasets for the tasks of one-class classification and active learning, where computing the uncertainty is an essential task. The experimental results highlight that we are able to compute classification uncertainties within microseconds even for large-scale datasets with tens of thousands of training examples.},
    doi = {10.1007/978-3-642-37444-9_40},
    groups = {gaussianprocesses,activelearning,largescale,incrementallearning},
    url = {https://doi.org/10.1007/978-3-642-37444-9_40},
}