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 Permutation Approach to Assess Confounding in Machine Lea..:
, In:
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
,
Chaibub Neto, Elias
;
Pratap, Abhishek
;
Perumal, Thanneer M.
... - p. 54-64 , 2019
Link:
https://dl.acm.org/doi/10.1145/3292500.3330903
RT T1 <cTitle>Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</cTitle>: T1 <Title>A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3330903&Exemplar=1&LAN=DE A1 Chaibub Neto, Elias A1 Pratap, Abhishek A1 Perumal, Thanneer M. A1 Tummalacherla, Meghasyam A1 Bot, Brian M. A1 Mangravite, Lara A1 Omberg, Larsson PB ACM YR 2019 K1 confounding K1 digital health K1 machine learning K1 permutation tests K1 restricted permutations K1 Computing methodologies K1 Machine learning K1 Learning paradigms K1 Supervised learning K1 Applied computing K1 Life and medical sciences K1 Health informatics K1 Mathematics of computing K1 Probability and statistics K1 Probabilistic inference problems SP 54 OP 64 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3292500.3330903 DO https://dl.acm.org/doi/10.1145/3292500.3330903 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
