288 pp. per issue
6 x 9, illustrated
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Neural Computation

January 1, 1997, Vol. 9, No. 1, Pages 161-183
(doi: 10.1162/neco.1997.9.1.161)
© 1997 Massachusetts Institute of Technology
Selecting Optimal Experiments for Multiple Output Multilayer Perceptrons
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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.