Decision Tree Complexity
Decision Tree Complexity is a computational complexity measure that quantifies the minimum number of queries or node evaluations required in the worst case by a decision tree to compute a Boolean function, distinguishing it from other complexity measures by its focus on the depth or structure of adaptive query strategies for function evaluation.
221
Publications
15.8K
Citations
406
Authors
209
Institutions
Publications per year
2017–2026
33
221
Quantum lower bounds by polynomials
Robert Beals, Harry Buhrman, Richard Cleve et al. · Journal of the ACM · 2001 · 659 citations
An Empirical Comparison of Pruning Methods for Decision Tree Induction
John Mingers · Machine Learning · 1989 · 638 citations · Full text
1–5 of 221
406
King Abdullah University of Science and Technology, Saudi Arabia
8 publications · 178 citations
8
h-index
7
h-index
5
h-index
5
h-index
King Abdullah University of Science and Technology, Saudi Arabia
5 publications · 114 citations
5
h-index
1–5 of 406
209
Princeton University
Princeton, United States
12 h-index
22 publications
1.1K citations

University of Calgary
Calgary, Canada
8 h-index
10 publications
1.2K citations
University of California San Diego
San Diego, United States
6 h-index
12 publications
333 citations

Yaroslav-the-Wise Novgorod State University
Veliky Novgorod, Russia
6 h-index
7 publications
129 citations
Hebrew University of Jerusalem
Jerusalem, Israel
6 h-index
7 publications
228 citations

Centre National de la Recherche Scientifique
Paris, France
6 h-index
13 publications
406 citations

Nizhny Novgorod State Pedagogical University
Nizhny Novgorod, Russia
5 h-index
7 publications
151 citations
Microsoft (United States)
Redmond, United States
5 h-index
7 publications
367 citations
1–8 of 209
14 publications · 1.2K citations
13
h-index
8 publications · 1.5K citations
10
h-index
JACM · 8 publications · 1.2K citations
8
h-index
Repository · 7 publications · 140 citations
7
h-index
6 publications · 146 citations
6
h-index
1–5