A modular architecture for efficient learning

A modular architecture for supervised learning is presented. Three ways to modularize the learning are investigated: (1) preprocessing the input by a self-organizing subnetwork to extract strong features from the data, (2) supervised feature extraction, and (3) an iterative learning cycle in which only one layer learns at a time, with the output-layer weights learned by an exact method. With this modular architecture, only a small fraction of connection weights is determined by the gradient-descent method. A series of computational experiments shows the superiority of the modular model in learning quality and speed. The author begins by considering the decomposition of a feedforward network into a feature-discovery and a supervised-learning modules, then he introduces supervised feature discovery, and finally he describes a modular backpropagation algorithm