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.

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