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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3
Learning Heterogeneous Functions from Sparse and Non-Uniform Samples
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
Dragoljub Pokrajac, Washington State University
Zoran Obradovic, Washington State University
A boosting-based method for centers placement in radial basis function networks (RBFN) is proposed. In addition, the influence of several methods for drawing random samples on the accuracy of RBFN is examined. The new method is compared to trivial, linear and non-linear regressors including the multilayer Perceptron and alternative RBFN learning algorithms and its advantages are demonstrated for learning heterogeneous functions from sparse and non-uniform samples.
Citation:
Dragoljub Pokrajac, Zoran Obradovic, "Learning Heterogeneous Functions from Sparse and Non-Uniform Samples," ijcnn, vol. 3, pp.3103, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000
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