Weight decay was proposed to reduce overfitting as it often appears in the learning tasks of artificial neural networks. In this paper weight decay is applied to a well defined model system based on a single layer perceptron, which exhibits strong overfitting. Since the optimal non-overfitting solution is known for this system, we can compare the effect of the weight decay with this solution. A strategy to find the optimal weight decay strength is proposed, which leads to the optimal solution for any number of examples.
CITATION STYLE
Bös, S. (1996). Optimal weight decay in a perceptron. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 551–556). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_94
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