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In machine learning, early stopping is a form of regularization used when a machine learning model (such as a neural network) is trained by on-line gradient descent. In early stopping, the training set is split into a new training set and a validation set. Gradient descent is applied to the new training set. After each sweep through the new training set, the network is evaluated on the validation set. The network with the best performance on the validation set is then used for actual testing.
Early stopping is a very common practice in neural network training and often produces networks that generalize well.
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