Super parameter optimization result.

<div><p>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...

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Bibliographic Details
Main Author:	Guomei Cui (20721578) (author)
Other Authors:	Chuanjun Wang (14802747) (author)
Published:	2025
Subjects:	Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified support vector machine convolutional neural network commonly used algorithms gradient boosting tree ensemble learning methods real game data decision tree optimization sprint pattern recognition decision tree pattern recognition tree structure existing methods recognition ability data collected xlink "> training data thus reducing test set super parameters study come study aims stride length step frequency sports states simulation environment reasonable selection random forest precision sensors may deviate generalization ability gbt ), future research fitting phenomenon computer simulation competition strategies adaptively adjusting 9 %, 0 %). 0 %)
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Super parameter optimization result.

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