To Describe or Construct Statistical Learning Models Using the Category-theoretical Language

Abstract: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.

Machine Learning (cs.LG)

arXiv:2608.03706 [cs.LG]

(or arXiv:2608.03706v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.03706 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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