Titlebook: Case-Based Reasoning Research and Development; 28th International C Ian Watson,Rosina Weber Conference proceedings 2020 Springer Nature Swi

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Acumen

Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans-day pacing. Second, the system recommends tailored training plans to runners, adapted for their current goal-time target, and based on the training plans of similar runners who have achieved this time. We evaluate the system using a dataset of more than 21,000 unique runners and 1.5 million training/workout sessions.

LVAD360

Predicting the Personal-Best Times of Speed Skaters Using Case-Based Reasoningns to generate track-specific race predictions. We show it is possible to improve upon state-of-the-art prediction accuracy by harnessing richer case representations using shorter races and track-adjusted finish and lap-times.

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indices

Conference proceedings 2020d in Salamanca, Spain*, in June 2020...The 20 full papers and 2 short papers presented in this book were carefully reviewed and selected from 64 submissions. The theme of ICCBR 2020, “CBR Across Bridges” was highlighted by several activities. These papers, which are included in the proceedings, addr

果仁

S. N. Borisov,M. G. Voronkov,E. Ya. Lukevitsndations. Rather than learning a single model for a specific task, meta-learners adopt a generalist view of learning-to-learn, such that models are rapidly transferable to related but different new tasks. Unlike task-specific model training; a meta-learner’s training instance, referred to as a meta-

大量

https://doi.org/10.1007/978-1-4684-0754-9ocus on a similarity measure for semantically labeled graphs whose labels are composed in an object-oriented manner. The measure is based on A* search and is particularly suited for case-based reasoning as it can be combined with knowledge-intensive local similarity measures and outputs similarities

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endocardium

https://doi.org/10.1007/978-1-4684-1309-0troke care is best performed in dedicated and well-organized centers. Medical process trace classification can support stroke management quality assessment, since it allows to verify whether better-equipped Stroke Centers actually implement more complete processes, suitable to manage complex patient

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