用范畴论语言重新理解经典统计学习模型
To Describe or Construct Statistical Learning Models Using the Category-theoretical Language
- 基于范畴论构建统计学习模型的统一描述框架
- 梳理经典模型与算法，提供数学视角的新理解
- 适合数学背景研究者跨入机器学习领域
统计学习是机器学习与人工智能领域的核心方向，产生了大量可广泛应用于现实问题的研究成果，并持续推动新课题的发展。本报告总结了若干经典统计学习模型与知名算法，尤其面向初学者，从范畴论的角度提供对统计学习模型的新理解。旨在吸引来自基础数学等其他领域的研究人员参与统计学习相关研究。
原文摘要 · Abstract (English)
Statistical learning is a fascinating field that has long been the mainstream of machine learning/artificial intelligence. A large number of results have been produced which can be widely applied to real-world problems. It also leads to many research topics and also stimulates new research. This report summarizes some classical statistical learning models and well-known algorithms, especially for amateurs, and provides a category-theoretic perspective on understanding statistical learning models. The aim is to attract researchers from other fields, including basic mathematics, to participate in the research related to statistical learning.
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