There has recently been widespread interest in the use of multiple models for classification and regression in the statistics and neural networks communities. The Hierarchical Mixture of Experts (HME) [1] has been successful in a number of regression problems, yielding significantly faster training through the use of the Expectation Maximisation algorithm. In this paper we extend the HME to classification and results are reported for three common classification benchmark tests: Exclusive-Or, N-input Parity and Two Spirals.
abbot is the hybrid connectionist hidden Markov model (HMM) large vocabulary continuous speech recognition system developed at Cambridge University Engineering Department. abbot makes eective use of the linear input network (LIN) adaptation technique to achieve speaker and channel adaptation. Although the LIN is eective at adapting to new speakers or a new environment (e.g. a dierent microphone), the transform is global over the input space. In this paper we describe a technique by which the transform may be made locally linear over dierent regions of the input space. The local linear transforms are combined by an additional network using a non-linear transform. This scheme falls naturally into the mixtures of experts framework.
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