Using Alpha-Beta Associative Memories to Learn and Recall RGB Images
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
IEEE Transactions on Neural Networks
TopoART: a topology learning hierarchical ART network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Continuous visual codebooks with a limited branching tree growing neural gas
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
A perceptual memory system for affordance learning in humanoid robots
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In this article, a novel unsupervised neural network combining elements from Adaptive Resonance Theory and topology-learning neural networks is presented. It enables stable on-line clustering of stationary and non-stationary input data by learning their inherent topology. Here, two network components representing two different levels of detail are trained simultaneously. By virtue of several filtering mechanisms, the sensitivity to noise is diminished, which renders the proposed network suitable for the application to real-world problems. Furthermore, we demonstrate that this network constitutes an excellent basis to learn and recall associations between real-world associative keys. Its incremental nature ensures that the capacity of the corresponding associative memory fits the amount of knowledge to be learnt. Moreover, the formed clusters efficiently represent the relations between the keys, even if noisy data is used for training. In addition, we present an iterative recall mechanism to retrieve stored information based on one of the associative keys used for training. As different levels of detail are learnt, the recall can be performed with different degrees of accuracy.