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- W2893441755 abstract "Gaussian processes constitute a very powerful and well-understood method for non-parametric regression and classification. In the classical framework, the training data consists of deterministic vector-valued inputs and the corresponding (noisy) measurements whose joint distribution is assumed to be Gaussian. In many practical applications, however, the inputs are either noisy, i.e., each input is a vector-valued sample from an unknown probability distribution, or the probability distributions are the inputs. In this paper, we address Gaussian process regression with inputs given in form of probability distributions and propose a framework that is based on distances between such inputs. To this end, we review different admissible distance measures and provide a numerical example that demonstrates our framework." @default.
- W2893441755 created "2018-10-05" @default.
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- W2893441755 date "2018-09-24" @default.
- W2893441755 modified "2023-09-27" @default.
- W2893441755 title "A Distance-based Framework for Gaussian Processes over Probability Distributions." @default.
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- W2893441755 hasPublicationYear "2018" @default.
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