Bias/variance analyses of mixtures-of-experts architectures.
This article investigates the bias and variance of mixtures-of-experts (ME) architectures. The variance of an ME architecture can be expressed as the sum of two terms: the first term is related to the variances of the expert networks that comprise the architecture and the second term is related to the expert networks' covariances. One goal of this article is to study and quantify a number of properties of ME architectures via the metrics of bias and variance. A second goal is to clarify the relationships between this class of systems and other systems that have recently been proposed. It is shown that in contrast to systems that produce unbiased experts whose estimation errors are uncorrelated, ME architectures produce biased experts whose estimates are negatively correlated.
Jacobs
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Department of Brain and Cognitive Sciences, University of Rochester, NY 14627, USA.
D000704:Analysis of Variance; D003198:Computer Simulation; D005103:Expert Systems; D007858:Learning; D015233:Models, Statistical; D015203:Reproducibility of Results