Titlebook: Combining Artificial Neural Nets; Ensemble and Modular Amanda J. C. Sharkey Book 1999 Springer-Verlag London Limited 1999 Ensembl.cognition

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Prinz Alwaleed bin Talal – der Anleger-Prinze optimal combination-weights for combining the networks. We describe an approach for treating collinearity by the proper selection of the component networks, and test two algorithms for selecting the components networks in order to improve the generalisation ability of the ensemble. We present expe

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Prinz Alwaleed bin Talal – der Anleger-Prinzd. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the .

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Boosting Using Neural Networks,ng works by iteratively constructing weak learners whose training set is conditioned on the performance of the previous members of the ensemble. In classification, we train neural networks using stochastic gradient descent and in regression, we train neural networks using conjugate gradient descent.

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A Genetic Algorithm Approach for Creating Neural Network Ensembles,prediction. An effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well; however, most existing techniques only indirectly address the problem of creating such a set. We present an algorithm

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