在数据分析和机器学习中对的应用:一篇评论
Salomé A Sepúlveda-Fontaine1, José M Amigó1
1Centro de Investigación Operativa, Universidad Miguel Hernández de Elche, 03202 Elche, Spain.
Entropy (Basel, Switzerland)
|January 8, 2025
概括
热力学中的概念,在数据分析和机器学习中对于描述概率分布至关重要. 本综述强调了的各种应用,证明了它在这些领域的力量和多功能性.
科学领域:
- 信息理论 信息理论
- 统计力学 统计力学
- 机器学习 机器学习
- 数据分析 数据分析
背景情况:
- 源于19世纪的热力学,并扩展到物理学和数学.
- 经典的积包括博尔兹曼-吉布斯,·诺曼,香农,科尔摩戈罗夫-西奈,以及拓学积.
- 为了特定的应用,已经开发出了许多变异.
研究的目的:
- 在数据分析和机器学习中审查各种测量的应用.
- 专注于特征概率质量分布的值.
- 根据Shannon和Khinchin,提供对的公理性表征.
主要方法:
- 审查古典和当代的测量方法.
- 选择一个具有代表性的输入组进行讨论.
- 专注于被定义为概率质量分布上的正函数的 entropies.
- 使用一种追溯到Shannon和Khinchin的公理性表征.
主要成果:
- 对于描述概率质量分布非常有效.
- 该综述展示了在数据分析和机器学习中的各种应用.
- 经典和新型度表现出显著的实用性.
结论:
- 是一种强大而通用的概念,在数据分析和机器学习中具有广泛的应用.
- 为理解和分析概率分布提供了一个强大的框架.
- 对进步这些领域而言,不断开发和应用测量是至关重要的.
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