A New Method to Assist Small Data Set Neural Network Learning

Artificial neural networks are relevant to solve large sample problems and the learning performance may not be good in small sample conditions. Inspired by applications of posterior probability, a new neural network learning method based on posterior probability (PPNN) is proposed to improve small data set learning accuracy in this paper. Together with the techniques of creating new learning samples to fill up the gaps between original samples and using support vector machine (SVM) to obtain posterior probabilities, a novel neural network model whose inputs include the samples and their posterior probabilities is constructed. Simulation experiment and two real data application results indicate that learning accuracy can be significantly improved by the proposed algorithm involving very small data set. It provides a new feasible way to assist small data set neural network learning.