Approximation capabilities of multilayer feedforward networks
Neural Networks
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Architecture-Independent Approximation of Functions
Neural Computation
Natural inspiration for artificial adaptivity: some neurocomputing experiences in robotics
UC'05 Proceedings of the 4th international conference on Unconventional Computation
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This paper investigates the functional invariance of neural network learning methods incorporating a complexity reduction mechanism, such as a regularizer. By functional invariance we mean the property of producing functionally equivalent minima as the size of the network grows, when the smoothing parameters are fixed. We study three different principles on which functional invariance can be based, and try to delimit the conditions under which each of them acts. We find out that, surprisingly, some of the most popular neural learning methods, such as weight-decay and input noise addition, exhibit this interesting property.