Partition-based and sharp uniform error bounds
Eric T. Bax · IEEE Transactions on Neural Networks · 1999
This paper develops probabilistic bounds on out-of-sample error rates for several classifiers using a single set of in-sample data. The bounds are based on probabilities over partitions of the union of in-sample and out-of-sample data into in-sample and out-of-sample data sets. The bounds apply when in-sample and out-of-sample data are drawn from the same distribution. Partition-based bounds are stronger than Vapnik-Chervonenkis (VC) bounds, but they require more computation.