title: Bayesian multioutput feedforward neural networks comparison: a conjugate prior approach.
abstract: A Bayesian method for the comparison and selection of multioutput feedforward neural network topology, based on the predictive capability, is proposed. As a measure of the prediction fitness potential, an expected utility criterion is considered which is consistently estimated by a sample-reuse computation. As opposed to classic point-prediction-based cross-validation methods, this expected utility is defined from the logarithmic score of the neural model predictive probability density. It is shown how the advocated choice of a conjugate probability distribution as prior for the parameters of a competing network, allows a consistent approximation of the network posterior predictive density. A comparison of the performances of the proposed method with the performances of usual selection procedures based on classic cross-validation and information-theoretic criteria, is performed first on a simulated case study, and then on a well known food analysis dataset.
authors.affiliation: UMR Analyse des Systèmes et Biométrie, INRA-ENSAM, 34060 Montpellier, France.
authors.forename: Vivien
authors.initials: V
authors.lastname: Rossi
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Vila