Titlebook: Reliability and Statistics in Transportation and Communication; Selected Papers from Igor Kabashkin,Irina Yatskiv,Olegas Prentkovskis Confe

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书目名称Reliability and Statistics in Transportation and Communication影响因子(影响力)




书目名称Reliability and Statistics in Transportation and Communication影响因子(影响力)学科排名




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书目名称Reliability and Statistics in Transportation and Communication网络公开度学科排名




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书目名称Reliability and Statistics in Transportation and Communication被引频次学科排名




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书目名称Reliability and Statistics in Transportation and Communication年度引用学科排名




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书目名称Reliability and Statistics in Transportation and Communication读者反馈学科排名

Common-Migraine

investigations of different equivalence notions between logic programs play a fundamental role for understanding modularity and optimization. While strong equivalence between two programs holds if they can be faithfully replaced by each other in any context (facts and rules), uniform equivalence am

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Marzieh Afsari,Nicola Ippolito,Lory Michelle Bresciani Mistrice,Guido Gentile investigations of different equivalence notions between logic programs play a fundamental role for understanding modularity and optimization. While strong equivalence between two programs holds if they can be faithfully replaced by each other in any context (facts and rules), uniform equivalence am

objection

Christopher Meyer,Laima Gerlitz,Gunnar Prause investigations of different equivalence notions between logic programs play a fundamental role for understanding modularity and optimization. While strong equivalence between two programs holds if they can be faithfully replaced by each other in any context (facts and rules), uniform equivalence am

gruelling

Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sources of uncertainty and they lack the efficient online

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Zokhid Rakhimov,Nadezhda Lvova,Svetlana Saksonova,Neli Abramishvilita is exiguous. While most of the previous papers work in the supervised setting, we study the more challenging case of positive-unlabeled transfer learning, where few positive labeled instances are available for both the source and the target domains. Specifically, we focus on the link prediction t

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