Virtual sample generation using a population of networks

摘要

Multilayer perceptrons have been shown to approximate any continuous functions with a desired precision. With insufficient training samples, however, the network can not learn the function properly and popular model selection methods such as cross validation can not be used. We propose a scheme to generate virtual samples using a population of networks. They are applied to regression problems and are shown to improve generalization and to solve the model selection problem at the same time.