Generate Different Neural Networks by Negative Correlation Learning

Abstract

AbstractThis paper describes two methods on how to generate different neural networks in an ensemble. One is based on negative correlation learning. The other is based on cross-validation with negative correlation learning, i.e., bagging with negative correlation learning. In negative correlation learning, all individual networks are trained simultaneously on the same training set. In bagging with negative correlation learning, different individual networks are trained on the different sampled data set with replacement from the training set. The performance and correct response sets are compared between two learning methods. The purpose of this paper is to find how to design more effective neural network ensembles.KeywordsNeural NetworkSimple AverageIndividual NetworkNeural Network EnsembleNegative Correlation LearningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.