We use case-based reasoning to help marathoners achieve a personal best for an upcoming race, by helping them to select an achievable goal-time and a suitable pacing plan. We evaluate several case representations and, using real-world race data, highlight their performance implications. Richer representations do not always deliver better prediction performance, but certain representational configurations do offer very significant practical benefits for runners, when it comes to predicting, and planning for, challenging goal-times during an upcoming race.
CITATION STYLE
Smyth, B., & Cunningham, P. (2018). An Analysis of Case Representations for Marathon Race Prediction and Planning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11156 LNAI, pp. 369–384). Springer Verlag. https://doi.org/10.1007/978-3-030-01081-2_25
Mendeley helps you to discover research relevant for your work.