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At present there is much effort to determine if emerging information processing devices could have a positive impact on the performance of non-Boolean/non-von Neumann computer architectures. We explore this topic here by specifically considering how emerging transistor technologies might impact cellular neural networks (CNNs). For CNNs, prior work suggests that new transistor structures could be employed...
In this paper, we propose two novel algorithms, ADABOOST.PL (Parallel ADABOOST) and LOGITBOOST.PL (Parallel LOGITBOOST), that facilitate simultaneous participation of multiple computing nodes to construct a boosted classifier. Our algorithms can induce boosted models whose generalization performance is close to the respective baseline classifier. By exploiting their own parallel architecture both...
The primary objective of disparities research is to model the differences across multiple groups and identify the groups that behave significantly different from each other. Independently generating various decision trees for different subsets of the data will not allow us to study the impact of the various attributes on these different subgroups. We propose a novel technique for inducing similar...
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