Error rate estimation via cros-validation and learning curve theory

摘要

Let L(sub)n be the expected error rate for a neural network trained with n examples. We present three novel estimators for L(sub)n that do not require additional examples. They operate in two steps. Firstly, L(sub)kn is estimated by training the model on subsets of size kn (0