Gaussian Processes for Regression Export

@inproceedings{Williams_96, abstract = {The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.}, author = {Williams, C. and Rasmussen, C.}, booktitle = {Advances in Neural Information Processing Systems 8}, citeulike-article-id = {3154206}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.8841}, keywords = {gaussian\_processes, learning, lwpr}, pages = {514--520}, posted-at = {2009-11-04 11:21:08}, priority = {2}, title = {Gaussian Processes for Regression}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.8841}, volume = {8}, year = {1996} }

TY - CONF ID - Williams_96 L3 - citeulike-article-id:3154206 N2 - The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results. EP - 520 TI - Gaussian Processes for Regression VL - 8 T2 - Advances in Neural Information Processing Systems 8 SP - 514 KW - gaussian_processes KW - learning KW - lwpr AU - Williams, C AU - Rasmussen, C PY - 1996/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.8841 ER -