Empirical Bias for Version Space

Nicolas, Jacques

Empirical Bias for Version Space

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Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 671-676.

Abstract

The ability to generalize remains one of the central issues of concept learning. A general generalization algorithm -the Candidate Elimination Algorithmexists but practical applications of this algorithm are still limited, due to its low convergence. The issue has shifted to the design of a useful "bias" limiting the size of the Version Space. This paper proposes a new kind of bias, called empirical bias, and a new general algorithm, ICE, for generalization in presence of bias. This proposition is founded on the concept of focus set, which provides a very flexible way to express expectations or constraints on the space of generalizations.

Jacques Nicolas. 1991. Empirical Bias for Version Space. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91. IJCAI, 671–676. https://ijcai.org/proceedings/1991/98

@inproceedings{ijcai1991p98,
  title     = {Empirical Bias for Version Space},
  author    = {Nicolas, Jacques},
  booktitle = {Proceedings of the Twelfth International Joint Conference on
               Artificial Intelligence, {IJCAI-91}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {John Mylopoulos and Ray Reiter},
  pages     = {671--676},
  year      = {1991},
  month     = {8},
  note      = {Learning Be Knowledge Acquisition},
  url       = {https://ijcai.org/proceedings/1991/98},
}

TY  - CPAPER
TI  - Empirical Bias for Version Space
AU  - Nicolas, Jacques
T2  - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91
PB  - International Joint Conferences on Artificial Intelligence
PY  - 1991
SP  - 671
EP  - 676
UR  - https://ijcai.org/proceedings/1991/98
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