Titlebook: High Performance Computing for Computational Science – VECPAR 2016; 12th International C Inês Dutra,Rui Camacho,Osni Marques Conference pro

茁壯成長

er dataset is described. The employed dataset discriminates between “wet fog” instances and “other weather conditions” instances, and it contains many missing data. Therefore, BNs were employed not only for classifying instances, but also for filling missing data. In addition, the Markov Blanket con

沒血色

Omar Ghattas,Tobin Isaac,Noémi Petra,Georg Stadlerrules as a minor extension to their normal duties, and are able to keep refining rules as KBS requirements evolve. Commercial RDR systems are now used routinely in some Chemical Pathology laboratories to provide interpretative comments to assist clinicians make the best use of laboratory reports. Th

Root494

Mariza Ferro,Giacomo Mc Evoy,Bruno Schulzerules as a minor extension to their normal duties, and are able to keep refining rules as KBS requirements evolve. Commercial RDR systems are now used routinely in some Chemical Pathology laboratories to provide interpretative comments to assist clinicians make the best use of laboratory reports. Th

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使堅硬

Toshiaki Hishinuma,Hidehiko Hasegawa,Teruo Tanakahe continuous parts of the problem are linearized and a Simplex scheme is applied. Alternatively, heuristic “bionic” optimisation methods can be used without having to linearize the problem. Weare going to demonstrate this approach by modelling power plant blocks with fast Neural Networks and optimi

CLEFT

Hartwig Anzt,Marc Baboulin,Jack Dongarra,Yvan Fournier,Frank Hulsemann,Amal Khabou,Yushan Wangh of the mill stands in the finishing train. In the past, these profiles were determined by human experts, based on their knowledge and experience. In previous work, the profiles were successfully optimised using a self-organising migration algorithm (SOMA). In this research, SASS, a novel heuristic

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Ronan Guivarch,Guillaume Joslin,Ronan Perrussel,Daniel Ruiz,Jean Tshimanga,Thomas Unferh of the mill stands in the finishing train. In the past, these profiles were determined by human experts, based on their knowledge and experience. In previous work, the profiles were successfully optimised using a self-organising migration algorithm (SOMA). In this research, SASS, a novel heuristic

殘廢的火焰

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