An Alternative Model for Mixtures of Experts

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Lei Xu, Michael Jordan, Geoffrey E. Hinton


We propose an alternative model for mixtures of experts which uses a different parametric form for the gating network. The modified model is trained by the EM algorithm. In comparison with earlier models-trained by either EM or gradient ascent-there is no need to select a learning stepsize. We report simulation experiments which show that the new architecture yields faster convergence. We also apply the new model to two problem domains: piecewise nonlinear function approximation and the combination of multiple previously trained classifiers.