• DocumentCode
    2508108
  • Title

    Gaussian Process Learning from Order Relationships Using Expectation Propagation

  • Author

    Wang, Ruixuan ; McKenna, Stephen J.

  • Author_Institution
    Sch. of Comput., Univ. of Dundee, Dundee, UK
  • fYear
    2010
  • fDate
    23-26 Aug. 2010
  • Firstpage
    605
  • Lastpage
    608
  • 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.
  • Keywords
    Gaussian processes; expectation-maximisation algorithm; learning (artificial intelligence); expectation propagation; gaussian process learning; marginal likelihood; order relationships; scalar function; Approximation methods; Benchmark testing; Covariance matrix; Fabrics; Gaussian processes; Machine learning; Gaussian processes; machine learning; preference learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2010 20th International Conference on
  • Conference_Location
    Istanbul
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4244-7542-1
  • Type

    conf

  • DOI
    10.1109/ICPR.2010.153
  • Filename
    5597452
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2508108