Title :
Variational Gaussian process classifiers
Author :
Gibbs, Mark N. ; MacKay, David J C
Author_Institution :
Cavendish Lab., Cambridge Univ., UK
fDate :
11/1/2000 12:00:00 AM
Abstract :
Gaussian processes are a promising nonlinear regression tool, but it is not straightforward to solve classification problems with them. In the paper the variational methods of Jaakkola and Jordan (2000) are applied to Gaussian processes to produce an efficient Bayesian binary classifier.
Keywords :
Bayes methods; Gaussian processes; covariance matrices; neural nets; pattern classification; Bayesian binary classifier; nonlinear regression tool; variational Gaussian process classifiers; variational methods; Bayesian methods; Covariance matrix; Gaussian approximation; Gaussian distribution; Gaussian processes; Monte Carlo methods; Neural networks; Parametric statistics; Predictive models; Probability distribution;
Journal_Title :
Neural Networks, IEEE Transactions on
