N-Tuple Features for OCR Revisited
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Neural Networks
The Effective VC Dimension of the n-tuple Classifier
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Coevolution in a large search space using resource-limited nash memory
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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Similarities between bootstrap aggregation (bagging) and N-tuple sampling are explored to propose a retina-free data-driven version of the N-tuple network, whose close analogies to aggregated regression trees, such as classification and regression trees (CART), lead to further architectural enhancements. Performance of the proposed algorithms is compared with the traditional versions of the N-tuple and CART networks on a number of regression problems. The architecture significantly outperforms conventional N-tuple networks while leading to more compact solutions and avoiding certain implementational pitfalls of the latter.