Titlebook: Neural Networks for Conditional Probability Estimation; Forecasting Beyond P Dirk Husmeier Book 1999 Springer-Verlag London Limited 1999 al

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Automatic Relevance Determination (ARD),o a weight group, and the distribution widths of the weight groups are adjusted during training by a method similar to Manhattan updating. A practical algorithm is derived, and an empirical demonstration shows that irrelevant inputs are detected and effectively switched off. The whole scheme was ins

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Summary,to predict a single future value as a function of a so-called lag vector of m past observations or measurements. The crucial requirement for the successful application of such a scheme is that the probability distribution of the targets conditional on the inputs is unimodal and symmetric. However, e

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Appendix: Derivation of the Hessian for the Bayesian Evidence Scheme,he derivation is based on an extended version of the EM algorithm, which allows the full Hessian to be decomposed into three additive components. The derivation of the first term, the Hessian of the EM error function U, is straightforward. The second term, the outer product of the gradient of the EM

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Random Vector Functional Link (RVFL) Networks,of the function to be approximated and subsequent evaluation of the integral by the Monte-Carlo approach. This is compared with the universal approximation capability of a standard MLP. The chapter terminates with a simple experimental illustration of the concept on a toy problem.

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The Bayesian Evidence Scheme for Model Selection,edastic noise on the target. The nature of the various Ockham factors included in the evidence is discussed. The chapter concludes with a critical evaluation of the numerical inaccuracies inherent in this scheme.

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1431-6854 cal findings on the generalisation performance of committeesConventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a time se