A resource-allocating network for function interpolation
Neural Computation
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Incremental learning methods with retrieving of interfered patterns
IEEE Transactions on Neural Networks
Heuristic pattern correction scheme using adaptively trained generalized regression neural networks
IEEE Transactions on Neural Networks
A fuzzy ARTMAP nonparametric probability estimator for nonstationary pattern recognition problems
IEEE Transactions on Neural Networks
Recent Advances in the Neocognitron
Neural Information Processing
Coding of objects in low-level visual cortical areas
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Learning location invariance for object recognition and localization
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Pixel-based machine learning in medical imaging
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
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This paper proposes a new neocognitron that accepts incremental learning, without giving a severe damage to old memories or reducing learning speed. The new neocognitron uses a competitive learning, and the learning of all stages of the hierarchical network progresses simultaneously.To increase the learning speed, conventional neocognitrons of recent versions sacrificed the ability of incremental learning, and used a technique of sequential construction of layers, by which the learning of a layer started after the learning of the preceding layers had completely finished. If the learning speed is simply set high for the conventional neocognitron, simultaneous construction of layers produces many garbage cells, which become always silent after having finished the learning. The proposed neocognitron with a new learning method can prevent the generation of such garbage cells even with a high learning speed, allowing incremental learning.