Neural Computation
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Sparse Distributed Memory
Experiments with the n-tuple Method of Pattern Recognition
IEEE Transactions on Computers
Temporal difference learning with interpolated table value functions
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Fast fingerprints classification only using the directional image
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
On a hybrid weightless neural system
International Journal of Bio-Inspired Computation
VG-RAM WNN approach to monocular depth perception
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Learning n-tuple networks for othello by coevolutionary gradient search
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Improving VG-RAM neural networks performance using knowledge correlation
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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The n-tuple recognition method is briefly reviewed, summarizing the main theoretical results. Large-scale experiments carried out on Stat-Log project datasets confirm this method as a viable competitor to more popular methods due to its speed, simplicity, and accuracy on the majority of a wide variety of classification problems. A further investigation into the failure of the method on certain datasets finds the problem to be largely due to a mismatch between the scales which describe generalization and data sparseness.