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
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;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.153
