Bayesian Model Comparison and Backprop Nets
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Bayesian Model Comparison and Backprop Nets
The Bayesian model comparison framework is reviewed, and the Bayesian Occam's razor is explained. This framework can be applied to feedforward networks, making possible (1) objective comparisons between solutions using alternative network architectures; (2) objective choice of magnitude and type of weight decay terms; (3) quantified estimates of the error bars on network parameters and on netwo...
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MacKay's Bayesian framework for backpropagation is conceptually appealing as well as practical. It automatically adjusts the weight decay parameters during training, and computes the evidence for each trained network. The evidence is proportional to our belief in the model. The networks with highest evidence turn out to generalise well. In this paper, the framework is extended to pruned nets, l...
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The conventional wisdom is that backprop nets with excess hidden units generalize poorly. We show that nets with excess capacity generalize well when trained with backprop and early stopping. Experiments suggest two reasons for this: 1) Overfitting can vary significantly in different regions of the model. Excess capacity allows better fit to regions of high non-linearity, and backprop often avo...
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Hinton [6] proposed that generalization in artificial neural nets should improve if nets learn to represent the domain's underlying regularities . Abu-Mustafa's hints work [1] shows that the outputs of a backprop net can be used as inputs through which domainspecific information can be given to the net . We extend these ideas by showing that a backprop net learning many related tasks at the sam...
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Hinton 6] proposed that generalization in artiicial neural nets should improve if nets learn to represent the domain's underlying regularities. Abu-Mustafa's hints work 1] shows that the outputs of a backprop net can be used as inputs through which domain-speciic information can be given to the net. We extend these ideas by showing that a backprop net learning many related tasks at the same tim...
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تاریخ انتشار 1992