The generalization ability of logistic regression with Markov sampling

Autor:	Zhiyong Qian, Wangsen Xiao, Shulan Hu
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	non-independent identically distributed samples logistic regression model uniformly ergodic markov chain algorithm generalization ability Mathematics QA1-939 Applied mathematics. Quantitative methods T57-57.97
Zdroj:	Electronic Research Archive, Vol 31, Iss 9, Pp 5250-5266 (2023)
Druh dokumentu:	article
ISSN:	2688-1594
DOI:	10.3934/era.2023267?viewType=HTML
Popis:	In the case of non-independent and identically distributed samples, we propose a new ueMC algorithm based on uniformly ergodic Markov samples, and study the generalization ability, the learning rate and convergence of the algorithm. We develop the ueMC algorithm to generate samples from given datasets, and present the numerical results for benchmark datasets. The numerical simulation shows that the logistic regression model with Markov sampling has better generalization ability on large training samples, and its performance is also better than that of classical machine learning algorithms, such as random forest and Adaboost.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/cf2eb66b04d547d29c23212c16ba809b Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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