Gaussian process learning from order relationships using expectation propagation

Ruixuan Wang, Stephen J. McKenna

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    Abstract

    A method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation propagation is used to obtain an approximation to the log marginal likelihood which is optimised using an analytical expression for its gradient. Experimental results show that the proposed method performs well compared with a previous method for Gaussian process preference learning.
    Original languageEnglish
    Title of host publicationProceedings - International Conference on Pattern Recognition
    Pages605-608
    Number of pages4
    DOIs
    Publication statusPublished - 2010
    Event20th International Conference on Pattern Recognition - Istanbul, Turkey
    Duration: 23 Aug 201026 Aug 2010

    Conference

    Conference20th International Conference on Pattern Recognition
    Abbreviated titleICPR 2010
    CountryTurkey
    CityIstanbul
    Period23/08/1026/08/10

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    Keywords

    • Analytical expressions
    • Expectation propagation
    • Gaussian processes
    • Marginal likelihood
    • Preference learning
    • Scalar function
    • Gaussian noise (electronic)
    • Pattern recognition
    • Gaussian distribution

    Cite this

    Wang, R., & McKenna, S. J. (2010). Gaussian process learning from order relationships using expectation propagation. In Proceedings - International Conference on Pattern Recognition (pp. 605-608) https://doi.org/10.1109/ICPR.2010.153