Zobrazeno 1 - 10
of 120
pro vyhledávání: '"Mendes-Moreira, João"'
Forecasting methods are affected by data quality issues in two ways: 1. they are hard to predict, and 2. they may affect the model negatively when it is updated with new data. The latter issue is usually addressed by pre-processing the data to remove
Externí odkaz:
http://arxiv.org/abs/2404.18273
This paper introduces an approach to predicting the next event in a soccer match, a challenge bearing remarkable similarities to the problem faced by Large Language Models (LLMs). Unlike other methods that severely limit event dynamics in soccer, oft
Externí odkaz:
http://arxiv.org/abs/2402.06820
Ensemble learning has been a focal point of machine learning research due to its potential to improve predictive performance. This study revisits the foundational work on ensemble error decomposition, historically confined to bias-variance-covariance
Externí odkaz:
http://arxiv.org/abs/2402.06818
This paper introduces an innovative application of Large Event Models (LEMs), akin to Large Language Models, to the domain of soccer analytics. By learning the language of soccer - predicting variables for subsequent events rather than words - LEMs f
Externí odkaz:
http://arxiv.org/abs/2402.06815
Computer Vision developments are enabling significant advances in many fields, including sports. Many applications built on top of Computer Vision technologies, such as tracking data, are nowadays essential for every top-level analyst, coach, and eve
Externí odkaz:
http://arxiv.org/abs/2301.07583
In soccer (or association football), players quickly go from heroes to zeroes, or vice-versa. Performance is not a static measure but a somewhat volatile one. Analyzing performance as a time series rather than a stationary point in time is crucial to
Externí odkaz:
http://arxiv.org/abs/2209.03882
Forecasting accuracy is reliant on the quality of available past data. Data disruptions can adversely affect the quality of the generated model (e.g. unexpected events such as out-of-stock products when forecasting demand). We address this problem by
Externí odkaz:
http://arxiv.org/abs/2106.13881
Publikováno v:
In Computers and Electronics in Agriculture January 2024 216
Hierarchical Qualitative Clustering: clustering mixed datasets with critical qualitative information
Clustering can be used to extract insights from data or to verify some of the assumptions held by the domain experts, namely data segmentation. In the literature, few methods can be applied in clustering qualitative values using the context associate
Externí odkaz:
http://arxiv.org/abs/2006.16701
Publikováno v:
In Engineering Applications of Artificial Intelligence November 2023 126 Part A