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
Active Sample Selection Through Sparse Neighborhood for Imb..:
, In:
2019 IEEE Symposium on Computers and Communications (ISCC)
,
Gu, Ping
;
Ling, Zhao
;
Shao, Si Yu
. - p. 1-6 , 2019
Link:
https://doi.org/10.1109/ISCC47284.2019.8969713
RT T1
2019 IEEE Symposium on Computers and Communications (ISCC)
: T1
Active Sample Selection Through Sparse Neighborhood for Imbalanced Datasets
UL https://suche.suub.uni-bremen.de/peid=ieee-8969713&Exemplar=1&LAN=DE A1 Gu, Ping A1 Ling, Zhao A1 Shao, Si Yu A1 Zhou, Meng YR 2019 SN 2642-7389 K1 Semisupervised learning K1 Manganese K1 Classification algorithms K1 Computers K1 Labeling K1 Training K1 Clustering algorithms K1 Active Learning K1 Imbalance Learning K1 Sample Selection K1 Classification SP 1 OP 6 LK http://dx.doi.org/https://doi.org/10.1109/ISCC47284.2019.8969713 DO https://doi.org/10.1109/ISCC47284.2019.8969713 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)