Sparse Logistic Regression: Comparison of Regularization and Bayesian Implementations

In knowledge-based systems, besides obtaining good output prediction accuracy, it is crucial to understand the subset of input variables that have most influence on the output, with the goal of gaining deeper insight into the underlying process. These requirements call for logistic model estimation...

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Detalhes bibliográficos

Principais autores:	Mattia Zanon, Giuliano Zambonin, Gian Antonio Susto, Seán McLoone
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2020-06-01
coleção:	Algorithms
Assuntos:	logistic regression sparse regularization relevance vector machine (RVM) least absolute shrinkage and selection operator (LASSO)
Acesso em linha:	https://www.mdpi.com/1999-4893/13/6/137
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