A unifying version-space representation

EN Smirnov*, HJ van den Herik, IG Sprinkhuizen-Kuyper

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


In this paper we consider the open problem how to unify version-space representations. We present a first solution to this problem, namely a new version-space representation called adaptable boundary sets (ABSs). We show that a version space can have a space of ABSs representations. We demonstrate that this space includes the boundary-set representation and the instance-based boundary-set representation; i.e., the ABSs unify these two representations.

We consider the task of learning ABSs as a task of identifying a proper representation within the space of ABSs depending on the applicability requirements given. This is demonstrated in a series of examples where ABSs are used to overcome the complexity problem of the boundary sets.

Original languageEnglish
Pages (from-to)47-76
Number of pages30
JournalAnnals of Mathematics and Artificial Intelligence
Issue number1
Publication statusPublished - May 2004


  • machine learning
  • concept learning
  • version spaces
  • boundary sets
  • instance-based boundary sets

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