I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Mixed gradient based fast learning algorithm with variable ..:
, In:
[Proceedings] Singapore ICCS/ISITA `92
,
Zengjun Xiang
;
Guangguo Bi
- p. 1419,1420,1421,1422 , 1992
Link:
https://doi.org/10.1109/ICCS.1992.255023
RT T1 <cTitle>[Proceedings] Singapore ICCS/ISITA `92</cTitle>: T1 <Title>Mixed gradient based fast learning algorithm with variable learning gain and selective updates for layered neural nets</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-255023&Exemplar=1&LAN=DE A1 Zengjun Xiang A1 Guangguo Bi YR 1992 K1 Gain K1 Backpropagation algorithms K1 Convergence K1 Gradient methods K1 Computational complexity K1 Optimization methods K1 Computer simulation K1 Neural networks K1 Bismuth K1 Nonhomogeneous media SP 1419,1420,1421,1422 LK http://dx.doi.org/https://doi.org/10.1109/ICCS.1992.255023 DO https://doi.org/10.1109/ICCS.1992.255023 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
