Regularization by Early Stopping in Single Layer Perceptron Training
نویسندگان
چکیده
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.
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