Add to ORKGThe paper proposes a novel construction algorithm for generalized Gaussian kernel regression models. Each kernel regressor in the generalized Gaussian kernel regression model has an individual diagonal covariance matrix, which is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. The standard orthogonal least squares algorithm is then used to select a sparse generalized kernel regression model from the resulting full regression matrix. Experimental results involving two real data sets demonstrate the effectiveness of the proposed regression modeling approach
The paper proposes to combine an orthogonal least squares (OLS) subset model selection with local re...
A computationally efficient method to estimate seemingly unrelated regression equations models with ...
Kernel methods are a well-studied approach for addressing regression problems by implicitly mapping ...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
Abstract—A novel technique is presented to construct sparse generalized Gaussian kernel regression m...
A novel technique is presented to construct sparse generalized Gaussian kernel regression models. Th...
Nonlinear system identification is considered using a generalized kernel regression model. Unlike th...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
Sparse regression modeling is addressed using a generalized kernel model in which kernel regressor h...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
A novel technique is presented to construct sparse Gaussian regression models. Unlike most kernel re...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
The generalised linear model (GLM) is the standard approach in classical statistics for regression t...
AbstractA computationally efficient method to estimate seemingly unrelated regression equations mode...
The paper proposes to combine an orthogonal least squares (OLS) subset model selection with local re...
A computationally efficient method to estimate seemingly unrelated regression equations models with ...
Kernel methods are a well-studied approach for addressing regression problems by implicitly mapping ...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
Abstract—A novel technique is presented to construct sparse generalized Gaussian kernel regression m...
A novel technique is presented to construct sparse generalized Gaussian kernel regression models. Th...
Nonlinear system identification is considered using a generalized kernel regression model. Unlike th...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
Sparse regression modeling is addressed using a generalized kernel model in which kernel regressor h...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
A novel technique is presented to construct sparse Gaussian regression models. Unlike most kernel re...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
The generalised linear model (GLM) is the standard approach in classical statistics for regression t...
AbstractA computationally efficient method to estimate seemingly unrelated regression equations mode...
The paper proposes to combine an orthogonal least squares (OLS) subset model selection with local re...
A computationally efficient method to estimate seemingly unrelated regression equations models with ...
Kernel methods are a well-studied approach for addressing regression problems by implicitly mapping ...