Can we ignore the compositional nature of compositional data by using deep learning aproaches?
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Authors
Author (Corporation)
Publication date
2021
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editor (Corporation)
Supervisor
Parent work
Book of short papers SIS 2021
Special issue
DOI of the original publication
Link
Series
Series number
Volume
Issue / Number
Pages / Duration
243-248
Patent number
Publisher / Publishing institution
Pearson
Place of publication / Event location
London
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Keywords
Subject (DDC)
Event
50th Scientific Meeting of the Italian Statistical Society (SIS 2021)
Exhibition start date
Exhibition end date
Conference start date
21.06.2021
Conference end date
25.06.2021
Date of the last check
ISBN
978-88-919-2736-1
ISSN
Language
English
Created during FHNW affiliation
No
Strategic action fields FHNW
Publication status
Published
Review
Expert editing/editorial review
Open access category
Closed
License
Citation
Templ, M. (2021). Can we ignore the compositional nature of compositional data by using deep learning aproaches? In C. Perna, N. Salvati, & F. Schirripa Spagnolo (Eds.), Book of short papers SIS 2021 (pp. 243–248). Pearson. https://irf.fhnw.ch/handle/11654/43326