Add to ORKGGaussian process regression allows a simple analytical treatment of exact Bayesian inference and has been found to provide good performance, yet scales badly with the number of training data. In this paper we compare several approaches towards scaling Gaussian processes regression to large data sets: the subset of representers method, the reduced rank approximation, online Gaussian processes, and the Bayesian committee machine. Furthermore we provide theoretical insight into some of our experimental results. We found that subset of representers methods can give good and particularly fast predictions for data sets with high and medium noise levels. On complex low noise data sets, the Bayesian committee machine achieves significantly better a...
In this report, we discuss the application and usage of Gaussian Process in Classification and Regre...
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the dat...
We explore ways to scale Gaussian processes (GP) to large datasets. Two methods with different theor...
Gaussian process (GP) methods have been widely studied recently, especially for large-scale systems ...
Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine l...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
Gaussian processes have proved to be useful and powerful constructs for the purposes of regression. ...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
Gaussian Process (GP) has become a common Bayesian inference framework and has been applied in many ...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
A significant problem of Gaussian process (GP) is its unfavorable scaling with a large amount of dat...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the dat...
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the dat...
Copyright © 2015 by the author(s).To scale Gaussian processes (GPs) to large data sets we introduce ...
In this report, we discuss the application and usage of Gaussian Process in Classification and Regre...
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the dat...
We explore ways to scale Gaussian processes (GP) to large datasets. Two methods with different theor...
Gaussian process (GP) methods have been widely studied recently, especially for large-scale systems ...
Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine l...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
Gaussian processes have proved to be useful and powerful constructs for the purposes of regression. ...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
Gaussian Process (GP) has become a common Bayesian inference framework and has been applied in many ...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
A significant problem of Gaussian process (GP) is its unfavorable scaling with a large amount of dat...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the dat...
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the dat...
Copyright © 2015 by the author(s).To scale Gaussian processes (GPs) to large data sets we introduce ...
In this report, we discuss the application and usage of Gaussian Process in Classification and Regre...
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the dat...
We explore ways to scale Gaussian processes (GP) to large datasets. Two methods with different theor...