| Author: | Carl Edward Rasmussen, Christopher K. I. Williams |
| URL: | http://www.gaussianprocess.org/gpml/ |
| Description: | Full content of a book that deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. |
| Keywords: | online, AI, machine learning |
