Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Incremental version-space merging: a general framework for concept learning
Incremental version-space merging: a general framework for concept learning
Artificial Intelligence
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Machine Learning
Machine Learning - Special issue on context sensitivity and concept drift
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Version spaces without boundary sets
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
k-version-space multi-class classification based on k-consistency tests
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
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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.