EXPERIMENTS WITH SIMPLE HEBBIAN-BASED LEARNING RULES IN PATTERN CLASSIFICATION TASKS
George F. Harpur and Richard W. Prager
This report presents a neural network architecture which performs pattern classification using a simple form of learning based on the Hebb rule. The work was motivated by the desires to decrease computational complexity and to maintain a greater degree of biological plausibility than most other networks designed to perform similar tasks. A method of pre-processing the inputs to provide a distributed representation is described. A scheme for increasing the power of the network using a layer of `feature detectors' is introduced: these use an unsupervised competitive learning scheme, again based on Hebbian learning. Simulation results from testing the networks on two `real-world' problems are presented, and compared to those produced by other types of neural network.
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