## Abstract

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 language | English |
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Pages (from-to) | 47-76 |

Number of pages | 30 |

Journal | Annals of Mathematics and Artificial Intelligence |

Volume | 41 |

Issue number | 1 |

DOIs | |

Publication status | Published - May 2004 |

## Keywords

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