@inproceedings{Rodner12:LGP,
    type = {inproceedings},
    key = {Rodner12:LGP},
    title = {Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels},
    author = {Erik Rodner and Alexander Freytag and Paul Bodesheim and Joachim Denzler},
    booktitle = {European Conference on Computer Vision (ECCV)},
    year = {2012},
    pages = {85-98},
    volume = {4},
    abstract = {We present how to perform exact large-scale multi-class Gaussian process classification with parameterized histogram intersection kernels. In contrast to previous approaches, we use a full Bayesian model without any sparse approximation techniques, which allows for learning in sub-quadratic and classification in constant time. To handle the additional model flexibility induced by parameterized kernels, our approach is able to optimize the parameters with large-scale training data. A key ingredient of this optimization is a new efficient upper bound of the negative Gaussian process log-likelihood. Experiments with image categorization tasks exhibit high performance gains with flexible kernels as well as learning within a few minutes and classification in microseconds for databases, where exact Gaussian process inference was not possible before.},
    doi = {10.1007/978-3-642-33765-9_7},
    groups = {largescale,gaussianprocesses},
    keywords = {gaussian processes, histogram intersection kernels, large-scale classification},
    url = {https://doi.org/10.1007/978-3-642-33765-9_7},
}