Open AccessThe Use of the Modified Semi-bounded Plug-in Algorithm to Compare Neural and Bayesian Classifiers Stability
Author(s) -
Ibtissem Ben Othman,
Faouzi Ghorbel
Publication year - 2022
Publication title -
international journal of neural networks and advanced applications
Language(s) - English
Resource type - Journals
ISSN - 2313-0563
DOI - 10.46300/91016.2022.9.2
Subject(s) - bounded function , artificial neural network , bayesian probability , stability (learning theory) , computer science , algorithm , artificial intelligence , machine learning , mathematics , mathematical analysis
Despite of the widespread use of the neural networks in the industrial applications, their mathematical formulation remains difficult to analyze. This explains a limited amount of work that formally models their classification volatility. Referring to the statistical point of view, we attempt in this work to evaluate the classical and Bayesian neural networks stability degree compared to the statistical methods stressing their error rate probability densities. The comparison based on this new criterion is performed using the modified semi-bounded Plug-in algorithm.