Kernel regression networks with local structural information and covariance volume adaptation

Authors:
J. Y. Goulermas;P. Liatsis;X. -J. Zeng
Affiliations:
Department of Electrical Engineering and Electronics, Brownlow Hill, University of Liverpool, Liverpool L69 3GJ, UK;Information and Biomedical Engineering Centre, School of Engineering and Mathematical Sciences, City University, London EC1V 0HB, UK;School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK
Venue:
Neurocomputing
Year:
2008

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Abstract

An improved Generalised Regression Neural Network is proposed for function approximation that incorporates kernels which adapt to the local structural information of the training data. Unlike the standard network, it allows bandwidth information to vary efficiently with each pattern in order to allow better adaptation to the local spatial arrangements of the nearest neighbours. The proposed network allows the use of structural information by employing full covariances with adaptive kernel volumes that are trained to form the optimum regression surfaces. Experiments show improved accuracy over the standard regression models with computationally efficient training.