An approach for constructing parsimonious generalized Gaussian kernel regression models

Intelligent Systems and Diagnostics Group, Department of Electronic and Computer Engineering, University of Portsmouth, Anglesea Building, Anglesea Road, Portsmouth PO1 3DJ, UK; School of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
Neurocomputing (Impact Factor: 1.63). 12/2004; DOI: 10.1016/j.neucom.2004.06.003
Source: DBLP

ABSTRACT The 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.

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