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
Considering Reliability of Deep Learning Function to Boost ..:
, In:
2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)
,
Gauerhof, Lydia
;
Hagiwara, Yuki
;
Schorn, Christoph
. - p. 249-254 , 2020
Link:
https://doi.org/10.1109/ISSREW51248.2020.00081
RT T1 <cTitle>2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)</cTitle>: T1 <Title>Considering Reliability of Deep Learning Function to Boost Data Suitability and Anomaly Detection</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9307744&Exemplar=1&LAN=DE A1 Gauerhof, Lydia A1 Hagiwara, Yuki A1 Schorn, Christoph A1 Trapp, Mario YR 2020 K1 Reliability K1 Training K1 Videos K1 Training data K1 Noise measurement K1 Correlation K1 Anomaly detection K1 data analysis K1 data suitability K1 anomaly detection SP 249 OP 254 LK http://dx.doi.org/https://doi.org/10.1109/ISSREW51248.2020.00081 DO https://doi.org/10.1109/ISSREW51248.2020.00081 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
