Machine Learning For Estimation
Machine Learning for Estimation is a scholarly research contribution that employs machine learning methodologies to model, predict, or infer temporal parameters or events within a timeline, distinguishing itself by leveraging data-driven algorithms to enhance the accuracy and adaptability of temporal estimation tasks.
124
Publications
8.6K
Citations
399
Authors
256
Institutions
Publications per year
2017–2026
109
Parents
124
1–5 of 124
399
3
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 399
256
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
10 publications
389 citations
Pontifícia Universidade Católica do Rio de Janeiro
Rio de Janeiro, Brazil
3 h-index
10 publications
1.2K citations
University of Illinois Urbana-Champaign
Urbana, United States
3 h-index
4 publications
276 citations
University of Chicago
Chicago, United States
3 h-index
4 publications
413 citations
Aalborg University
Aalborg, Denmark
3 h-index
6 publications
143 citations
Karlsruhe Institute of Technology
Karlsruhe, Germany
2 h-index
4 publications
198 citations
Universität Hamburg
Hamburg, Germany
2 h-index
4 publications
110 citations

University of Vaasa
Vaasa, Finland
2 h-index
5 publications
308 citations
1–8 of 256
Repository · 8 publications · 260 citations
8
h-index
Repository · 6 publications · 113 citations
5
h-index
3 publications · 84 citations
3
h-index
JOCN · 3 publications · 118 citations
3
h-index
Repository · 3 publications · 71 citations
3
h-index
1–5