Sparse Gaussian Processes using Pseudo-inputs

@misc{citeulike:1846806, abstract = {We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudo-input points, which we learn by a gradient based optimization. We take M N , where N is the number of real data points, and hence obtain a sparse regression method which has N) training cost and ) prediction cost per test case. We also find hyperparameters of the covariance function in the same joint optimization. The method can be viewed as a Bayesian...}, author = {Snelson, E. and Ghahramani, Z. }, booktitle = {NIPS 18}, citeulike-article-id = {1846806}, journal = {Neural Information Processing Systems 18}, keywords = {approximate, gaussianprocess, gp, machinelearning, sparse}, posted-at = {2008-01-28 17:58:24}, priority = {3}, title = {Sparse Gaussian Processes using Pseudo-inputs}, url = {http://citeseer.ist.psu.edu/738600.html}, year = {2005} }

TY - GEN ID - citeulike:1846806 L3 - citeulike-article-id:1846806 TI - Sparse Gaussian Processes using Pseudo-inputs T2 - NIPS 18 JF - Neural Information Processing Systems 18 N2 - We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudo-input points, which we learn by a gradient based optimization. We take M N , where N is the number of real data points, and hence obtain a sparse regression method which has N) training cost and ) prediction cost per test case. We also find hyperparameters of the covariance function in the same joint optimization. The method can be viewed as a Bayesian... KW - approximate KW - gaussianprocess KW - gp KW - machinelearning KW - sparse AU - Snelson, E AU - Ghahramani, Z PY - 2005/// UR - http://citeseer.ist.psu.edu/738600.html ER -