Výsledky vyhledávání - "Robberechts, Pieter"

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Elastic Product Quantization for Time Series

Autor: Robberechts, Pieter, Meert, Wannes, Davis, Jesse

Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements. Therefore, techniques have been proposed to generate compact similarity-preserving representations of time series, enabling

Externí odkaz: http://arxiv.org/abs/2201.01856

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Leaving Goals on the Pitch: Evaluating Decision Making in Soccer

Autor: Van Roy, Maaike, Robberechts, Pieter, Yang, Wen-Chi, De Raedt, Luc, Davis, Jesse

Publikováno v: 2021 MIT Sloan Sports Analytics Conference

Analysis of the popular expected goals (xG) metric in soccer has determined that a (slightly) smaller number of high-quality attempts will likely yield more goals than a slew of low-quality ones. This observation has driven a change in shooting behav

Externí odkaz: http://arxiv.org/abs/2104.03252

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Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach

Autor: Robberechts, Pieter, Derie, Rud, Berghe, Pieter Van den, Gerlo, Joeri, De Clercq, Dirk, Segers, Veerle, Davis, Jesse

Publikováno v: Gait & Posture, Volume 84, 2021, Pages 87-92

Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. Howe

Externí odkaz: http://arxiv.org/abs/1910.13372

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A Bayesian Approach to In-Game Win Probability in Soccer

Autor: Robberechts, Pieter, Van Haaren, Jan, Davis, Jesse

Publikováno v: In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021) 3512-3521

In-game win probability models, which provide a sports team's likelihood of winning at each point in a game based on historical observations, are becoming increasingly popular. In baseball, basketball and American football, they have become important

Externí odkaz: http://arxiv.org/abs/1906.05029

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Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data

Autor: Bekker, Jessa, Robberechts, Pieter, Davis, Jesse

Publikováno v: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2019 (ECMLPKDD 2019)

Most positive and unlabeled data is subject to selection biases. The labeled examples can, for example, be selected from the positive set because they are easier to obtain or more obviously positive. This paper investigates how learning can be ena BH

Externí odkaz: http://arxiv.org/abs/1809.03207

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