relation: http://eprints.imtlucca.it/1751/
title: Can Dictionary-Based Computational Models Outperform the Best Linear Ones? 
creator: Gnecco, Giorgio
creator: Kůrková, Věra
creator: Sanguineti, Marcello
subject: QA75 Electronic computers. Computer science
description: Approximation capabilities of two types of computational models are explored: dictionary-based models (i.e., linear combinations of n -tuples of basis functions computable by units belonging to a set called “dictionary”) and linear ones (i.e., linear combinations of n fixed basis functions). The two models are compared in terms of approximation rates, i.e., speeds of decrease of approximation errors for a growing number n of basis functions. Proofs of upper bounds on approximation rates by dictionary-based models are inspected, to show that for individual functions they do not imply estimates for dictionary-based models that do not hold also for some linear models. Instead, the possibility of getting faster approximation rates by dictionary-based models is demonstrated for worst-case errors in approximation of suitable sets of functions. For such sets, even geometric upper bounds hold. 
publisher: Elseviers
date: 2011
type: Article
type: PeerReviewed
identifier:   Gnecco, Giorgio and Kůrková, Věra and Sanguineti, Marcello  Can Dictionary-Based Computational Models Outperform the Best Linear Ones?  Neural Networks , 24 (8).  881 - 887.  ISSN 0893-6080      (2011)  
relation: http://www.sciencedirect.com/science/article/pii/S0893608011001560
relation: 10.1016/j.neunet.2011.05.014