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

凹槽

Demonstration of the Model Performance on the Benchmark Problems,ce plot of the network predictions allows the attainment of a deeper understanding of the training process. For the double-well problem, the prediction performance of the DSM network is compared with different alternative approaches, and is found to achieve results comparable to those of the best al

MEET

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爭(zhēng)吵

蓋他為秘密

A simple Bayesian regularisation scheme, mode of their posterior distribution. Conjugate priors for the various network parameters are introduced, which give rise to regularisation terms that can be viewed as a generalisation of simple weight decay. It is shown how the posterior mode can be found with a slightly modified version of the EM

freight

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Entirety

Network Committees and Weighting Schemes,cation or by simple averaging in regression, but one can also use a weighted combination of the networks. The first section of this chapter summarises the main ideas of a recent study by Krogh and Vedelsby on network committees for simple interpolation tasks. The generalisation performance of the co

搬運(yùn)工

Demonstration: Committees of Networks Trained with Different Regularisation Schemes,on performance on the regularisation method and the weighting scheme is studied. For a single-model predictor, application of the Bayesian evidence scheme is found to lead to superior results. However, when using network committees, under-regularisation can be advantageous, since it leads to a large