Supervised Incremental Learning with the Fuzzy ARTMAP Neural Network

Abstract. Automatic pattern classifiers that allow for on-line incremental learning can adapt internal class models efficiently in response to new information without retraining from the start using all training data and without being subject to catastrophic forgeting. In this paper, the performance of the fuzzy ARTMAP neural network for supervised incremental learning is compared to that of supervised batch learning. An experimental protocole is presented to assess this network's potential for incremental learning of new blocks of training data, in terms of generalization error and resource requirements, using several synthetic pattern recognition problems. The advantages and drawbacks of training fuzzy ARTMAP incrementally are assessed for different data block sizes and data set structures. Overall results indicate that error rate of fuzzy ARTMAP is significantly higher when it is trained through incremental learning than through batch learning. As the size of training blocs dec...

• Type: Conference
• Year: 2008
• Where: ANNPR
• Authors: Jean-François Connolly, Eric Granger, Robert Sabourin
• Keywords: ANNPR 2008, Fuzzy ARTMAP, Incremental Learning, Neural Networks, On-line Incremental Learning