Corpus ID: 10098704

Mixtures of Gaussian Processes

@inproceedings{Tresp2000MixturesOG,
  title={Mixtures of Gaussian Processes},
  author={Volker Tresp},
  booktitle={NIPS},
  year={2000}
}
  • Volker Tresp
  • Published in NIPS 2000
  • Computer Science
  • We introduce the mixture of Gaussian processes (MGP) model which is useful for applications in which the optimal bandwidth of a map is input dependent. The MGP is derived from the mixture of experts model and can also be used for modeling general conditional probability densities. We discuss how Gaussian processes - in particular in form of Gaussian process classification, the support vector machine and the MGP model--can be used for quantifying the dependencies in graphical models. 
    174 Citations
    Infinite mixtures of multivariate Gaussian processes
    • Shiliang Sun
    • Computer Science, Mathematics
    • 2013 International Conference on Machine Learning and Cybernetics
    • 2013
    • 23
    • PDF
    Variational Mixture of Gaussian Process Experts
    • 70
    • PDF
    Variational Mixtures of Gaussian Processes for Classification
    • 3
    • PDF
    Pseudo Independent Conditional Approximation for Training the Mixtures of Gaussian Processes
    • Jiahui Luo, Jinwen Ma
    • Computer Science
    • 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
    • 2019
    A Precise Hard-Cut EM Algorithm for Mixtures of Gaussian Processes
    • 18
    • Highly Influenced
    Hierarchical Gaussian process mixtures for regression
    • 99
    • PDF
    An Efficient EM Approach to Parameter Learning of the Mixture of Gaussian Processes
    • 23
    • Highly Influenced

    References

    SHOWING 1-10 OF 14 REFERENCES
    Bayesian Classification With Gaussian Processes
    • 701
    • PDF
    Gaussian Process Networks
    • 58
    • PDF
    Nonlinear Markov Networks for Continuous Variables
    • 34
    • PDF
    The generalized Bayesian committee machine
    • 23
    • PDF
    Adaptive Mixtures of Local Experts
    • 3,567
    • Highly Influential
    • PDF
    Probabilistic Methods for Support Vector Machines
    • 70
    • PDF
    Lernen der Struktur nichtlinearer Abhängigkeiten mit graphischen Modellen
    • 6
    • Highly Influential
    Advances in Neural Information Processing Systems 12
    • 149
    Bayesian Classi£cation with Gaussian Processes
    • IEEE Transactions on Pattern Analysis and Machine Intelligence,
    • 1998