Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Learning from data with bounded inconsistency
Proceedings of the seventh international conference (1990) on Machine learning
Incremental version-space merging
Proceedings of the seventh international conference (1990) on Machine learning
Incremental Version-Space Merging: A General Framework for Concept Learning
Incremental Version-Space Merging: A General Framework for Concept Learning
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
Automatic Rule Learning for Resource-Limited MT
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
An Algebra for Inductive Query Evaluation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Recovering from errors during programming by demonstration
Proceedings of the 13th international conference on Intelligent user interfaces
Polynomial-time learning with version spaces
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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
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Mitchell's version-space approach to inductive concept learning has been highly influential in machine learning, as it formalizes inductive concept learning as a search problem-to identify some concept definition out of a space of possible definitions. This paper lays out some theoretical underpinnings of version spaces. It presents the conditions under which an arbitrary set of concept definitions in a concept description language can be represented by boundary sets, which is a necessary condition for a set of concept definitions to be a version space. Furthermore, although version spaces can be intersected and unioned (version spaces are simply sets, albeit with special structure), the result need not be a version space; this paper also presents the conditions under which such intersection and union of two version spaces yields a version space (i.e., representable by boundary sets). Finally, the paper shows how the resulting boundary sets after intersections and unions can be computed from the initial boundary sets, and proves the algorithms correct.