Add to ORKGAbstract—A novel technique is presented to construct sparse generalized Gaussian kernel regression models. The proposed method appends regressors in an incremental modeling by tuning the mean vector and diagonal covariance matrix of an individual Gaussian regressor to best fit the training data, based on a correlation criterion. It is shown that this is identical to incre-mentally minimizing the modeling mean square error (MSE). The optimization at each regression stage is carried out with a simple search algorithm re-enforced by boosting. Experimental results obtained using this technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing parsimonious models. Index Term...
Nonlinear system identification is considered using a generalized kernel regression model. Unlike th...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
In this paper we propose a new basis selection criterion for building sparse GP regression models th...
A novel technique is presented to construct sparse generalized Gaussian kernel regression models. Th...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
A novel technique is presented to construct sparse Gaussian regression models. Unlike most kernel re...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
Abstract: A novel technique is proposed for the incremental construction of sparse radial basis func...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
We present a new Gaussian process (GP) regression model whose covariance is parameterized by the th...
A novel technique is proposed for the incremental construction of sparse radial basis function (RBF)...
Nonlinear system identification is considered using a generalized kernel regression model. Unlike th...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
In this paper we propose a new basis selection criterion for building sparse GP regression models th...
A novel technique is presented to construct sparse generalized Gaussian kernel regression models. Th...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
A novel technique is presented to construct sparse Gaussian regression models. Unlike most kernel re...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
Abstract: A novel technique is proposed for the incremental construction of sparse radial basis func...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
We present a new Gaussian process (GP) regression model whose covariance is parameterized by the th...
A novel technique is proposed for the incremental construction of sparse radial basis function (RBF)...
Nonlinear system identification is considered using a generalized kernel regression model. Unlike th...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
In this paper we propose a new basis selection criterion for building sparse GP regression models th...