A hybrid neural network learning system

This paper presents a hybrid learning system for learning and designing of neural network ensembles based on supervised learning and unsupervised learning. There are two terms in the performance function where one term is optimised by supervised learning, and the other by unsupervised learning. Through supervised learning, each neural network in an ensemble could learn target output as much as possible from the training data. By unsupervised learning, all neural networks learn simultaneously to cover different parts of training data in order to learn how to subdivide the whole training data. The learning behaviour of the hybrid learning system is studied based on correlations among the individual networks in the ensemble.