Computational geometry: an introduction
Computational geometry: an introduction
Learning decision trees from random examples needed for learning
Information and Computation
Finding the Optimal Variable Ordering for Binary Decision Diagrams
IEEE Transactions on Computers
Lower bounds on learning decision lists and trees
Information and Computation
Exact learning when irrelevant variables abound
Information Processing Letters
On P versus NP CO-NP for decision trees and read-once branching programs
Computational Complexity
Branching programs and binary decision diagrams: theory and applications
Branching programs and binary decision diagrams: theory and applications
The nonapproximability of OBDD minimization
Information and Computation
Logic Synthesis and Verification Algorithms
Logic Synthesis and Verification Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Complexity measures and decision tree complexity: a survey
Theoretical Computer Science - Complexity and logic
The complexity of minimizing and learning OBDDs and FBDDs
Discrete Applied Mathematics
Machine Learning
Hardness of Approximating Problems on Cubic Graphs
CIAC '97 Proceedings of the Third Italian Conference on Algorithms and Complexity
Decision trees: a recent overview
Artificial Intelligence Review
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Decision trees are representations of discrete functions with widespread applications in, e.g., complexity theory and data mining and exploration. In these areas it is important to obtain decision trees of small size. The minimization problem for decision trees is known to be NP-hard. In this paper the problem is shown to be even hard to approximate up to any constant factor under the assumption PNP.