An evaluation of factors influencing Bayesian learning systems

Autor:	E L, Eisenstein, F, Alemi
Rok vydání:	1993
Předmět:	Artificial Intelligence Humans Learning Bayes Theorem Medical Informatics Applications Models Theoretical Prognosis Probability Research Article
Zdroj:	Proceedings. Symposium on Computer Applications in Medical Care.
ISSN:	0195-4210
Popis:	This paper examines the influences of situational and model factors upon the accuracy of Bayesian learning systems. In particular, it is concerned with the impact of variations in training sample size, number of attributes, choice of Bayesian model, and criteria for excluding model attributes upon the overall accuracy of the simple and proper Bayes models.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=pmid________::63362afaaee260834ff758095727db94 https://pubmed.ncbi.nlm.nih.gov/8130520 Zobrazit plný text záznamu