In computational learning theory, sample exclusion dimensions arise in the study of exact concept learning with queries.
[1] In algorithmic learning theory, a concept over a domain X is a Boolean function over X.
A partial approximation S of a concept c is a Boolean function over
C, denoted by S is a partial approximation S of c such that C contains at most one extension to S. If we have observed a specifying set for some concept w.r.t.
C, then we have enough information to verify a concept in C with at most one more mind change.