Early Stopping
Early Stopping is a regularization technique in machine learning that halts the training process prematurely based on the performance on a validation set to prevent overfitting by avoiding excessive iterations that lead to model specialization on training data.
582
Publications
42K
Citations
1.8K
Authors
749
Institutions
Publications per year
2017–2026
242
582
An Overview of Overfitting and its Solutions
Ying Xue · Journal of Physics Conference Series · 2019 · 2.2K citations · Full text
On Early Stopping in Gradient Descent Learning
Yuan Yao, Lorenzo Rosasco, Andrea Caponnetto · Constructive Approximation · 2007 · 999 citations
1–5 of 582
1.8K
5
h-index
5
h-index
5
h-index
4
h-index
4
h-index
1–5 of 1.8K
749
Stanford University
Stanford, United States
10 h-index
21 publications
1.1K citations
University of Wisconsin–Madison
Madison, United States
10 h-index
14 publications
717 citations
University of California, Berkeley
Berkeley, United States
10 h-index
19 publications
1.9K citations
Columbia University
New York, United States
9 h-index
12 publications
541 citations
Harvard University
Cambridge, United States
9 h-index
14 publications
901 citations
Massachusetts Institute of Technology
Cambridge, United States
9 h-index
23 publications
2.7K citations
University of Michigan
Ann Arbor, United States
8 h-index
11 publications
375 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
8 h-index
23 publications
1.5K citations
1–8 of 749
Repository · 76 publications · 3.7K citations
32
h-index
31 publications · 1.5K citations
20
h-index
12 publications · 988 citations
11
h-index
10 publications · 545 citations
10
h-index
9 publications · 301 citations
9
h-index
1–5