ALT2017

Mon, 16 Oct 2017 00:00:00 +0000

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.

1993-01-ALT

Fri, 01 Jan 1993 00:00:00 +0000

The Vapnik-Chervonenkis (VC) dimension is known to be the crucial measure of the polynomial-sample learnability in the PAC-learning model. This paper investigates the complexity of computing VC-dimension of a concept class over a finite learning domain. We consider a…

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ALT2017

1993-01-ALT