Abstract
Ensemble encoding employs multiple, overlapping receptive fields to yield a distributed representation of analog signals. The effect of ensemble encoding on learning in multi-layer perceptron (MLP) networks is examined by applying it to a neural learning benchmark, sonar signal classification. Results suggest that, when used to encoded input patterns, ensemble encoding can accelerate learning and improve classification accuracy in MLP networks.
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