OSA-a topological algorithm for constructing two-layer neural networks

Presents a constructive training algorithm for supervised neural networks: OSA (Oil-Spot Algorithm). It builds a two-layer neural network by involving successively binary examples. Its main learning rule, based on topological theorems on the cuts of a binary hypercube, is discussed. A convenient treatment of real-valued data is possible by means of a suitable real-to-binary codification. For binary target functions that have efficient halfspace union representations, the constructed networks are optimized in terms of number of neurons with respect to other constructive algorithms, as shown