Control of complexity in learning with perturbed inputs
This paper considers the problem of function approximation from scattered data when using multilayered perceptrons. We present a new algorithm based on the principle of Inputs Perturbation, improving the generalization performances of backpropagation. In this algorithm, a new parameter is introduced in order to allow a control of the complexity of the t. This parameter balances the bias versus the variance independently of the smoothness of the inputs density estimate. The tuning capacity of the algorithm is illustrated by experimental evidences.