Neural networks and logistic regression. II.

DOI10.1016/0167-9473(95)00033-XzbMath1077.62525OpenAlexW2999850990WikidataQ58434192 ScholiaQ58434192MaRDI QIDQ1351184

Martin Schumacher, Werner Vach, Reinhard Roßner

Publication date: 27 February 1997

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-9473(95)00033-x

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Neural networks and logistic regression: Part I ⋮ Logistic regression using covariates obtained by product-unit neural network models ⋮ Multilogistic regression by means of evolutionary product-unit neural networks ⋮ Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data ⋮ Applied regression analysis bibliography update 1994-97

Uses Software

• alr3
• SenSrivastava

Cites Work

• Selecting models from data: artificial intelligence and statistics IV. Selected papers presented at the fourth international workshop on artificial intelligence and statistics held in January 1993
• Multilayer feedforward networks are universal approximators
• Universal approximation bounds for superpositions of a sigmoidal function
• Testing for Qualitative Interactions between Treatment Effects and Patient Subsets
• Approximation by superpositions of a sigmoidal function
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