Information Merging in Neural Modelling
نویسندگان
چکیده
The paper addresses the problem of deening a neural system which combines pieces of independent information available in both the data and parameters spaces. The problem is approached in the framework of the probabilistic interpretation of neural modelling: in order to take into account the indetermination associated to the training process, a distribution in the weight space is associated to each solution, and the network resulting from the combination is obtained by merging the distributions associated to the diierent solutions. The eeectiveness of the proposed procedure is shown by applying it to feedforward neural networks trained on a classiication task.
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تاریخ انتشار 2007