IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Remote sensing image fusion based on adaptive RBF neural network
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
An excellent feature selection model using gradient-based and point injection techniques
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Classifying unbalanced pattern groups by training neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint sets in Rn is considered. The network parameters can be determined by minimizing an error function that measures the degree of success in the recognition of a given number of training patterns. In this paper, taking into account the specific feature of classification problems, where the goal is to obtain that the network outputs take values above or below a fixed threshold, we propose an approach alternative to the classical one that makes use of the least-squares error function. In particular, the problem is formulated in terms of a system of nonlinear inequalities, and a suitable error function, which depends only on the violated inequalities, is defined. Then, a training algorithm based on this formulation is presented. Finally, the results obtained by applying the algorithm to two test problems are compared with those derived by adopting the commonly used least-squares error function. The results show the effectiveness of the proposed approach in RBF network training for pattern recognition, mainly in terms of computational time saving