Titlebook: Artificial Intelligence XXXVII; 40th SGAI Internatio Max Bramer,Richard Ellis Conference proceedings 2020 Springer Nature Switzerland AG 20

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書目名稱Artificial Intelligence XXXVII影響因子(影響力)




書目名稱Artificial Intelligence XXXVII影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence XXXVII網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence XXXVII網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence XXXVII被引頻次




書目名稱Artificial Intelligence XXXVII被引頻次學(xué)科排名




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書目名稱Artificial Intelligence XXXVII年度引用學(xué)科排名




書目名稱Artificial Intelligence XXXVII讀者反饋




書目名稱Artificial Intelligence XXXVII讀者反饋學(xué)科排名

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https://doi.org/10.1007/978-981-97-4962-1 trade-off between accuracy and interpretability. Fuzzy Cognitive Maps (FCMs) and their extensions are recurrent neural networks that have been partially exploited towards fulfilling such a goal. However, the interpretability of these neural systems has been confined to the fact that both neural con

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AMBI

https://doi.org/10.1007/978-981-97-4962-1Belief Revision add/delete axioms or delete/add preconditions to rules, respectively. Reformation repairs them by changing the . of the faulty theory. Unfortunately, the ABC system overproduces repair suggestions. Our aim is to prune these suggestions to leave only a Pareto front of the optimal ones

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Kindle

https://doi.org/10.1007/978-981-97-4962-1prohibitive when tasked with creating models that are sensitive to personal nuances in human movement, explicitly present when performing exercises and when it is infeasible to collect training data to cover the whole target population. Accordingly, learning personalised models with few data remains

鍵琴

https://doi.org/10.1007/978-981-97-4962-1ppens through trial and error using explorative methods, such as .-greedy. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. Model-based RL learns a model of the environment for learning the policy while model-free approac

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https://doi.org/10.1007/978-981-97-4962-1energy consumption constraints. Tsetlin Machines (TMs) are a recent approach to machine learning that has demonstrated significantly reduced energy usage compared to neural networks alike, while performing competitively accuracy-wise on several benchmarks. However, TMs rely heavily on energy-costly