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Selecting optimal experiments for multiple output multilayer perceptrons.

L M Belue·K W Bauer·D W Ruck
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Abstract

Where should a researcher conduct experiments to provide training data for a multilayer perceptron? This question is investigated, and a statistical method for selecting optimal experimental design points for multiple output multilayer perceptrons is introduced. Multiple class discrimination problems are examined using a framework in which the multilayer perceptron is viewed as a multivariate nonlinear regression model. Following a Bayesian formulation for the case where the variance-covariance matrix of the responses is unknown, a selection criterion is developed. This criterion is based on the volume of the joint confidence ellipsoid for the weights in a multilayer perceptron. An example is used to demonstrate the superiority of optimally selected design points over randomly chosen points, as well as points chosen in a grid pattern. Simplification of the basic criterion is offered through the use of Hadamard matrices to produce uncorrelated outputs.

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AlgorithmsBayes TheoremDiscrimination, PsychologicalNeural Networks, ComputerNonlinear DynamicsJournal Article

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BibTeXRIS

L M Belue, K W Bauer, D W Ruck. 1997-01-01. Selecting optimal experiments for multiple output multilayer perceptrons.. https://doi.org/10.1162/neco.1997.9.1.161

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