On Model Improvement Algorithms—Generalised Linear Models and Neural Networks

We propose a generic approach to stochastic model improvement by first introducing an archetypal algorithm based on error minimisation and establishing two results on the weak convergence of the probability laws associated with the models under improvement. We then present two concrete instances of...

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Hlavní autoři:	Manuel L. Esquível, Nadezhda P. Krasii, Raquel M. Gaspar
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2026-02-01
Edice:	Mathematics
Témata:	stochastic models model improvement algorithms algorithm convergence weak convergence of distributions generalised linear models neural networks
On-line přístup:	https://www.mdpi.com/2227-7390/14/3/561
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