Construction of RBF Classifiers with Tunable Units using Orthogonal Forward Selection Based on Leave-One-Out Misclassification Rate - researchr publication related

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Sheng Chen, Chris J. Harris, Xia Hong. Construction of RBF Classifiers with Tunable Units using Orthogonal Forward Selection Based on Leave-One-Out Misclassification Rate. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, BC, Canada, 16-21 July 2006. pages 3358-3362, IEEE, 2006. [doi]

The following publications are possibly variants of this publication:

Fast Kernel Classifier Construction Using Orthogonal Forward Selection to Minimise Leave-One-Out Misclassification RateXia Hong, Sheng Chen, Chris J. Harris. icic 2006: 106-114 [doi]

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rateXia Hong, Sheng Chen, Chris J. Harris. ijsysc, 39(2):119-125, 2008. [doi]

Construction of Tunable Radial Basis Function Networks Using Orthogonal Forward SelectionSheng Chen, Xia Hong, Bing Lam Luk, Chris J. Harris. tsmc, 39(2):457-466, 2009. [doi]

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularizationSheng Chen, Xia Hong, Chris J. Harris. tsmc, 34(4):1708-1717, 2004. [doi]

Kernel Classifier Construction Using Orthogonal Forward Selection and Boosting With Fisher Ratio Class Separability MeasureSheng Chen, Xunxian Wang, Xia Hong, Chris J. Harris. tnn, 17(6):1652-1656, 2006. [doi]

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