The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports

This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come from high-precision sensors and computer simulation,...

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Veröffentlicht in:	PloS one Jg. 20; H. 2; S. e0317414
Hauptverfasser:	Cui, Guomei, Wang, Chuanjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 13.02.2025 Public Library of Science (PLoS)
Schlagworte:	Accuracy Adaptive algorithms Algorithms Analysis Artificial neural networks Athletes Biology and Life Sciences Biomechanical Phenomena - physiology Biomechanics Classification Computer and Information Sciences Computer Simulation Computer-generated environments Convolutional Neural Networks Data mining Decision making Decision trees Efficiency Engineering and Technology Ensemble learning Evaluation Humans Hunting Learning algorithms Machine Learning Medicine and Health Sciences Methods Neural networks Optimization Parameters Pattern recognition Pattern Recognition, Automated - methods Physical Sciences Random Forest Research and Analysis Methods Runners (Sports) Running - physiology Sensors Signal processing Simulation methods Sports Sports injuries Sprinting Support Vector Machine Support vector machines Track & field Track and Field - physiology Vision systems China
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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