Optimal average case estimation in Hilbert norms
type: Item
instance of: scholarly article
title: Optimal average case estimation in Hilbert norms
zbMATH Open document ID: 0980.93014
DOI: 10.1007/PL00009874
published in: MCSS. Mathematics of Control, Signals, and Systems
publication date: 2001-02-22T00:00:00Z
review text: Two local average case error measures of algorithms are considered. They are based on noisy information and on Hilbert norms, as well as information spaces. The optimal algorithm is defined. Formulas for its two local errors are provided. They explicitly exhibit the influence of factors such as information, information errors, norms in the considered spaces, etc. Based on the error expression, the problem of selecting the optimal approximating space is formulated. The solution is given with a specific formula for the error depending on the equivalence of a certain matrix defined by information and norms.
reviewed by: Klaus Ehemann
Mathematics Subject Classification ID: 93B30
zbMATH DE Number: 1569517
zbMATH Keywords: identification algorithm
zbMATH Keywords: average case error measures of algorithms
zbMATH Keywords: information spaces
zbMATH Keywords: optimal algorithm
zbMATH Keywords: information errors
author: Bolesław Kacewicz
MaRDI profile type: Publication
full work available at URL: pl00009874
OpenAlex ID: W2059586842
description: scientific article; zbMATH DE number 1569517