一个基于机器学习的模型,用于对越野滑雪运动员的全面评估和分类
Qujunli Chen1, Zhirui Liu1, Dacheng He2
1Harbin Sport University.
Research quarterly for exercise and sport
|October 10, 2025
概括
本研究引入了一种新的机器学习模型,将越野滑雪者分为五个技能级别. 该模型使用先进的技术进行更准确和科学的运动员评估,优于手动评分.
科学领域:
- 运动科学 运动科学 运动科学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 准确的运动员评估对于越野滑雪的表现改善至关重要.
- 当前的方法可能缺乏准确性来捕捉微妙的技能差异.
- 以数据为导向的方法可以提供更客观的评估.
研究的目的:
- 开发和验证一种新的机器学习模型,用于将越野滑雪者分为五个不同的技能水平.
- 整合各种运动员指标 (身体,心理,专业技能) 以进行全面的评估.
- 证明拟议模型在现有方法和手动评估上具有优越性.
主要方法:
- 一个混合机器学习模型,结合了多层感知器 (MLP),注意力机制和支持矢量机器 (SVM).
- 在MLP模型中,处理单个指标类别 (身体,心理,技能) 来提取特征.
- 注意力机制动态重量和保险丝特征,随后由SVM进行最终分类.
主要成果:
- 该模型获得了0.9306的高F1得分,显著超过了0.10.0的基线方法.
- 与传统的手动分级相比,表现出更高的性能,提供了更科学的评估.
- 注意力机制在动态特征加权和融合中被证明是有效的,用于准确的分类.
结论:
- 拟议的机器学习模型为越野滑雪者的技能水平评估提供了一个强大而准确的系统.
- 通过注意力机制的动态功能集成提高了分类精度.
- 这种方法为体育分析中手动评估提供了一个更客观,更科学,更可靠的替代方案.
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