Pruning from Adaptive Regularization
1994; The MIT Press; Volume: 6; Issue: 6 Linguagem: Inglês
10.1162/neco.1994.6.6.1223
ISSN1530-888X
AutoresLars Kai Hansen, Carl Edward Rasmussen,
Tópico(s)Probabilistic and Robust Engineering Design
ResumoInspired by the recent upsurge of interest in Bayesian methods we consider adaptive regularization. A generalization based scheme for adaptation of regularization parameters is introduced and compared to Bayesian regularization. We show that pruning arises naturally within both adaptive regularization schemes. As model example we have chosen the simplest possible: estimating the mean of a random variable with known variance. Marked similarities are found between the two methods in that they both involve a “noise limit,” below which they regularize with infinite weight decay, i.e., they prune. However, pruning is not always beneficial. We show explicitly that both methods in some cases may increase the generalization error. This corresponds to situations where the underlying assumptions of the regularizer are poorly matched to the environment.
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