A constructive algorithm for binary neural networks: the oil-spot algorithm.
This paper presents a constructive training algorithm for supervised neural networks. The algorithm relies on a topological approach, based on the representation of the mapping of interest onto the binary hypercube of the input space. It dynamically constructs a two-layer neural network by involving successively binary examples. A convenient treatment of real-valued data is possible by means of a suitable real-to-binary codification. In the case of target functions that have efficient halfspace union representations, simulations show the constructed networks result optimized in terms of number of neurons.
Frattale Mascioli
F M
FM
INFOCOM Dept., Rome Univ.
Martinelli
G
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