Learning Random Log-Depth Decision Trees under Uniform Distribution

Authors:
Jeffrey C. Jackson;Rocco A. Servedio
Affiliations:
-;-
Venue:
SIAM Journal on Computing
Year:
2005

Citing 0
Cited 3

Learning Random Monotone DNF

APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
Lower bounds on learning random structures with statistical queries

ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Learning random monotone DNF

Discrete Applied Mathematics

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Abstract

We consider three natural models of random logarithmic depth decision trees over Boolean variables. We give an efficient algorithm that for each of these models learns all but an inverse polynomial fraction of such trees using only uniformly distributed random examples from {0,1}n. The learning algorithm constructs a decision tree as its hypothesis.