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
A Feature Selection Method based on Sparse Regularization D..:
, In:
2023 4th International Conference on Computer Engineering and Application (ICCEA)
,
Sun, Junjun
;
Chen, Sibao
- p. 675-679 , 2023
Link:
https://doi.org/10.1109/ICCEA58433.2023.10135182
RT T1
2023 4th International Conference on Computer Engineering and Application (ICCEA)
: T1
A Feature Selection Method based on Sparse Regularization Deep Neural Network
UL https://suche.suub.uni-bremen.de/peid=ieee-10135182&Exemplar=1&LAN=DE A1 Sun, Junjun A1 Chen, Sibao YR 2023 SN 2159-1288 K1 Deep learning K1 Dimensionality reduction K1 Neural networks K1 Feature extraction K1 Classification algorithms K1 Optimization K1 feature selection K1 neural network K1 pattern classification SP 675 OP 679 LK http://dx.doi.org/https://doi.org/10.1109/ICCEA58433.2023.10135182 DO https://doi.org/10.1109/ICCEA58433.2023.10135182 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)