Adaptative training of the non-linear single-layer perceptron can lead to the Euclidean distance classifier and later to the standard Fisher linear discriminant function. On the way between these two classifiers one has a regularized discriminant analysis. That is equivalent to the "weight decay" regularization term added to the cost function. Thus early stopping plays a role of regularization of the network.
CITATION STYLE
Raudys, S., & Cibas, T. (1996). Regularization by early stopping in single layer pereeptron training. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 77–82). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_17
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