Neural Networks
Word association norms, mutual information, and lexicography
Computational Linguistics
A resource-allocating network for function interpolation
Neural Computation
Mixture Density Estimation Based on Maximum Likelihood and Sequential Test Statistics
Neural Processing Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
A note on the utility of incremental learning
AI Communications
Recurrent neural network architecture with pre-synaptic inhibition for incremental learning
Neural Networks - 2006 Special issue: Neurobiology of decision making
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IPNN: An Incremental Probabilistic Neural Network for Function Approximation and Regression Tasks
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
An Evolving System Based on Probabilistic Neural Network
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper proposes a technique, called Evolving Probabilistic Neural Network (ePNN), that presents many interesting features, including incremental learning, evolving architecture, the capacity to learn continually throughout its existence and requiring that each training sample be used only once in the training phase without reprocessing. A series of experiments was performed on data sets in the public domain; the results indicate that ePNN is superior or equal to the other incremental neural networks evaluated in this paper. These results also demonstrate the advantage of the small ePNN architecture and show that its architecture is more stable than the other incremental neural networks evaluated. ePNN thus appears to be a promising alternative for a quick learning system and a fast classifier with a low computational cost.