For Valid Generalization, the Size of the Weights is More Important Than the Size of the Network (1997)
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BibTeX
@MISC{Bartlett97forvalid,
author = {Peter L. Bartlett},
title = {For Valid Generalization, the Size of the Weights is More Important Than the Size of the Network},
year = {1997}
}
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Abstract
This paper shows that if a large neural network is used for a pattern classification problem, and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. More specifically, consider an `-layer feed-forward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A. The misclassification probability converges to an error estimate (that is closely related to squared error on the training set) at rate O((cA) `(`+1)=2 p (log n)=m) ignoring log factors, where m is the number of training patterns, n is the input dimension, and c is a constant. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early st...
Keyphrases
valid generalization training pattern generalization performance pattern classification problem layer feed-forward network sigmoid unit error estimate large neural network misclassification probability weight decay input dimension log factor early st training example neural network small weight
