Measurements of generalisation based on information geometry

Zhu, Huaiyu and Rohwer, Richard (1995). Measurements of generalisation based on information geometry. Annals of Mathematics And artificial Intelligence ,

Abstract

Neural networks are statistical models and learning rules are estimators. In this paper a theory for measuring generalisation is developed by combining Bayesian decision theory with information geometry. The performance of an estimator is measured by the information divergence between the true distribution and the estimate, averaged over the Bayesian posterior. This unifies the majority of error measures currently in use. The optimal estimators also reveal some intricate interrelationships among information geometry, Banach spaces and sufficient statistics.

Divisions: ?? 13770100JJ ??
Additional Information: Copyright of SpringerLink
Uncontrolled Keywords: neural networks,Bayesian,information geometry,estimator,error,Banach
Published Date: 1995-07-02

Download

[img]

Export / Share Citation


Statistics

Additional statistics for this record