Add to ORKGA novel technique is presented to construct sparse Gaussian regression models. Unlike most kernel regression modelling methods, which restrict kernel means to the training input data and use a fixed common variance for all the regressors, the proposed technique can tune the mean vector and diagonal covariance matrix of individual Gaussian regressor to best fit the training data based on the correlation between the regressor and the training data. An efficient repeated weighted optimization method is developed based on boosting with the correlation criterion to append regressors one by one in incremental regression modelling. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to th...
The computation required for Gaussian process regression with n training examples is about O(n^3) du...
An automatic algorithm is derived for constructing kernel density estimates based on a regression ap...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
A novel technique is presented to construct sparse generalized Gaussian kernel regression models. Th...
Abstract—A novel technique is presented to construct sparse generalized Gaussian kernel regression m...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
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...
A novel technique is proposed for the incremental construction of sparse radial basis function (RBF)...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
A unified approach is proposed for sparse kernel data modelling that includes regression and classif...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
We present a new Gaussian process (GP) regression model whose covariance is parameterized by the th...
The computation required for Gaussian process regression with n training examples is about O(n^3) du...
An automatic algorithm is derived for constructing kernel density estimates based on a regression ap...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
A novel technique is presented to construct sparse generalized Gaussian kernel regression models. Th...
Abstract—A novel technique is presented to construct sparse generalized Gaussian kernel regression m...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
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...
A novel technique is proposed for the incremental construction of sparse radial basis function (RBF)...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
A unified approach is proposed for sparse kernel data modelling that includes regression and classif...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
We present a new Gaussian process (GP) regression model whose covariance is parameterized by the th...
The computation required for Gaussian process regression with n training examples is about O(n^3) du...
An automatic algorithm is derived for constructing kernel density estimates based on a regression ap...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...