Incremental Learning with Respect to New Incoming Input Attributes
Neural Processing Letters
Ordered incremental training for GA-based classifiers
Pattern Recognition Letters
An adaptable transport protocol based on Genetic Algorithms
International Journal of Information and Communication Technology
Adaptive Training of a Kernel-Based Representative and Discriminative Nonlinear Classifier
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Multiuser detection using backpropagation networks for CDMA systems
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
Incremental learning in a fuzzy intelligent system
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Evolving logic networks with real-valued inputs for fast incremental learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
IEEE Transactions on Neural Networks
Local coupled feedforward neural network
Neural Networks
Adaptive training of a kernel-based nonlinear discriminator
Pattern Recognition
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How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a new incremental learning method for pattern recognition, called the “incremental backpropagation learning network”, which employs bounded weight modification and structural adaptation learning rules and applies initial knowledge to constrain the learning process. The viability of this approach is demonstrated for classification problems including the iris and the promoter domains