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Sports Athletes' Performance Prediction Model Based on Machine Learning Algorithm

机译：基于机器学习算法的体育运动员绩效预测模型

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摘要

In order to accurately predict the athletes' performance, combined with the specific changes of athletes' performance, a performance prediction model based on machine learning algorithm is proposed. The current research status of sports athletes' performance modeling and prediction is analyzed, and the current athletes' performance prediction model is found. The shortcomings of the model are analyzed. The reason for the low prediction accuracy of the model is analyzed. Then the chaotic theory is used to process the historical data of the athletes, and the hidden rules are found. Finally, using the machine-learning algorithm supporting vector machine to design athlete performance prediction-model is adopted, and the particle swarm is adopted. The algorithm accelerates and optimizes the training of the model. The results on the test set show that compared with the current athlete performance prediction-model, the athletes' performance prediction results of the designed model are more reliable, and the athletes' prediction accuracy is higher, which can be applied to the development of sports science training plan.
机译：为了准确预测运动员的性能，结合运动员性能的具体变化，提出了一种基于机器学习算法的性能预测模型。分析了运动运动员性能建模和预测的当前研究现状，发现了当前运动员的绩效预测模型。分析了模型的缺点。分析了模型预测精度的原因。然后，混沌理论用于处理运动员的历史数据，并找到隐藏的规则。最后，采用支持向量机的机器学习算法设计运动员性能预测模型，采用粒子群。算法加速并优化模型的培训。测试集的结果表明，与当前运动员性能预测模型相比，设计模型的运动员的性能预测结果更可靠，运动员的预测精度较高，这可以应用于运动的发展科学培训计划。

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