26 October 2013 A novel backward elimination algorithm for construction of RBF neural networks
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Proceedings Volume 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 892115 (2013) https://doi.org/10.1117/12.2030735
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
A novel backward elimination algorithm (BEA) based on the energy contributions of the non-orthogonal (coupled) regressor vectors is introduced for radial basis function (RBF) neural network construction. This algorithm builds RBF model by eliminating the minimum contribution regressor term among all candidates according to mean predictedresidual- sums-of-squares (PRESS) error. It first generates an initial model using computationally affordable batch learning and then updated it by a sequent learning with new training samples arriving. During the whole learning, the network architecture always remains the most optimal. This also can assure a better RBF network even if the RBF original basis is non-orthogonal. The effectiveness of new algorithm is demonstrated by the simulated results.
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Peng Zhou, Zhu Yang, "A novel backward elimination algorithm for construction of RBF neural networks ", Proc. SPIE 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 892115 (26 October 2013); doi: 10.1117/12.2030735; https://doi.org/10.1117/12.2030735
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