Competitive learning algorithms for vector quantization
Neural Networks
Learning in a competitive network
Neural Networks
Statistical theory of learning curves under entropic loss criterion
Neural Computation
Association with Multi-dendritic Radial Basis Units
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
Divided-Data Analysis in a Financial Case Classification with Multi-dendritic Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Neural Computation
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Two new entropic measures are proposed: the A-entropy and Eentropy, which are compared during competitive training processes in multiplayer networks with radial basis units. The behavior of these entropies are good indicators of the orthogonality reached in the layer representations for vector quantization tasks. The proposed E-entropy is a good candidate to be considered as a measure of the training level reached for all layers in the same training process. Both measures would serve to monitorize the competitive learning in this kind of neural model, that is usually implemented in the hidden layers of the Radial Basis Functions networks.