The Central Classifier Bound - A New Error Bound for the Classifier Chosen by Early Stopping
Eric T. Bax, Zehra Çataltepe, Joe Sill · 1997
Training with early stopping is the following process. Partition the in sample data into training and validation sets Begin with a random classifier g_(1-). Use an iterative method to decrease the error rate on the training data. Record the classifier at each iteration producing a series of snapshots g_1....g_M. Evaluate the error rate of each snapshot over the validation data. Deliver a minimum validation error classifier. g^* as the result of training.