Flexible and efficient Gaussian process models for machine learning
Gaussian process (GP) models are widely used to perform Bayesian nonlinear regression and classification tasks that are central to many machine learning problems. A GP is nonparametric, meaning that the complexity of the model grows as more data points are received. Another attractive feature is the...
| Main Author: | |
|---|---|
| Published: |
University College London (University of London)
2007
|
| Subjects: | |
| Online Access: | http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445905 |