A comparison of fuzzy ARTMAP and Gaussian ARTMAP neural networks for incremental learning

Abstract— Automatic pattern classifiers that allow for incremental learning can adapt internal class models efficiently in response to new information, without having to retrain from the start using all the cumulative training data. In this paper, the performance of two such classifiers – the fuzzy ARTMAP and Gaussian ARTMAP neural networks – are characterize and compared for supervised incremental learning in environments where class distributions are fixed. Their potential for incremental learning of new blocks of training data, after previously been trained, is assessed in terms of generalization error and resource requirements, for several synthetic pattern recognition problems. The advantages and drawbacks of these architectures are discussed for incremental learning with different data block sizes and data set structures. Overall results indicate that Gaussian ARTMAP is the more suitable for incremental learning as it usually provides an error rate that is comparable to...

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Eric Granger, Jean-François Connolly, Robert Sabourin
• Keywords: Artificial Intelligence, Gaussian ARTMAP, IJCNN 2008, Incremental Learning, Supervised Incremental Learning