Evaluation of Adaptive Mixtures of Competing Experts
Steven Nowlan and Geoffrey E. Hinton
Department of Computer Science
University of Toronto
We compare the performance of the modular architecture, composed of
competing expert networks, suggested by Jacobs, Jordan, Nowlan and Hinton (1991) to the
performance of a single back-propagation network on a complex, but low-dimensional, vowel
recognition task. Simulations reveal that this system is capable of uncovering interesting
decompositions in a complex task. The type of decomposition is strongly influenced
by the nature of the input to the gating network that decides which expert to use for each
case. The modular architecture also exhibits consistently better generalization on
many variations of the task.
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Advances in Neural Information Processing Systems 3 (1991).
D.S. Touretzky, M.C. Mozer and M.E. Hasselmo. MIT Press.
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