Can Dictionary-based Computational Models Outperform the Best Linear Ones?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00360287" target="_blank" >RIV/67985807:_____/11:00360287 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.neunet.2011.05.014" target="_blank" >http://dx.doi.org/10.1016/j.neunet.2011.05.014</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neunet.2011.05.014" target="_blank" >10.1016/j.neunet.2011.05.014</a>
Alternative languages
Result language
angličtina
Original language name
Can Dictionary-based Computational Models Outperform the Best Linear Ones?
Original language 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 modelsis demonstrated for worst-case errors in approximation of suitable sets of functions. For such sets, even geometric upper bounds hold.
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/OC10047" target="_blank" >OC10047: Analysis of Intelligent Computational Distributed Systems</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Networks
ISSN
0893-6080
e-ISSN
—
Volume of the periodical
24
Issue of the periodical within the volume
8
Country of publishing house
GB - UNITED KINGDOM
Number of pages
7
Pages from-to
881-887
UT code for WoS article
000295105700012
EID of the result in the Scopus database
—