Statistical inference
Stochastic processes
A new definition of sensitivity for RBFNN and its applications to feature reduction
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
Sensitivity analysis of multilayer perceptron to input and weight perturbations
IEEE Transactions on Neural Networks
Sensitivity analysis of multilayer perceptron with differentiable activation functions
IEEE Transactions on Neural Networks
The selection of weight accuracies for Madalines
IEEE Transactions on Neural Networks
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Considering the inputs of a feed-forward neural network as random variables, this paper proposes a definition of partial derivative of a function with respect to a random variable in the probability measure space. The mathematical expectation of the mean square or absolute value of the partial derivative is regarded as a type of measure of the network's sensitivity, which extends Zurada's sensitivity definition of networks in Zurada et al, [Perturbation method for deleting redundant inputs of perceptron networks, Neurocomputing 14 (1997) 177-193] from the certain environment to the stochastic environment. Furthermore, for the purpose of network's redundant feature deletion or feature selection, the new sensitivity measure is applied to the sensitivity analysis of Radial Basis Function Neural Networks (RBFNNs). The feasibility and the effectiveness of the sensitivity approach to redundant feature deletion are illustrated.