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An efficient query learning algorithm for zero-suppressed binary decision diagrams
Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:360-371, 2017.
Abstract
A ZDD is a directed acyclic graph that represents a family of sets over a fixed universe set. In this paper, we propose an algorithm that learns zero-suppressed binary decision diagrams (ZDDs) using membership and equivalence queries. If the target ZDD has n nodes and the cardinality of the universe is m, our algorithm uses n equivalence queries and at most n(⌊logm⌋+4n) membership queries to learn the target ZDD.