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Emergent on-line learning in min-max modular neural networks

This paper presents a novel online supervised learning model called emergent online learning for pattern classification. The model involves three mechanisms: decomposition of an online learning problem at each time step into a reasonable number of linearly separable problems; parallel learning of these linearly separable problems by using linear threshold gates; and integration of the trained linear threshold gates into a min-max modular network. Two simple emergent laws are used to control both the problem decomposition and solution integration. The advantages of the model are very fast learning speed, guaranteed convergence, high modularity, and parallelism.