Machine learning of inductive bias
Machine learning of inductive bias
Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Information Processing Letters
Combining empirical and analytical learning with version spaces
Proceedings of the sixth international workshop on Machine learning
Limitations on inductive learning
Proceedings of the sixth international workshop on Machine learning
Conceptual clustering in biology: application and perspectives
Proceedings of the conference on Data analysis, learning symbolic and numeric knowledge
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
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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.