Sparse Logistic Regression: Comparison of Regularization and Bayesian Implementations

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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Bibliographic Details
Main Authors: Mattia Zanon, Giuliano Zambonin, Gian Antonio Susto, Seán McLoone
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Algorithms
Subjects:
logistic regression
sparse regularization
relevance vector machine (RVM)
least absolute shrinkage and selection operator (LASSO)
Online Access:https://www.mdpi.com/1999-4893/13/6/137

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https://www.mdpi.com/1999-4893/13/6/137

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