Projection Learning and Graceful Degradation
We have presented in (Weigl et al. 1992 - 1993 b) a paradigm in which we consider neural networks such as Multi-layer Perceptrons as bases in a function space; the basis functions are the functions computed by the hidden layer neurons, and the function approximated by the network is the projection of the function to be approximated onto the manifold spanned by these basis functions. We have presented a learning algorithm based on that paradigm, which consists in shifting the manifold spanned by that base in function space in such a way that the distance to the function to be approximated is minimal.
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