Abstract
This paper continues a previous work using stochastic heuristics to extract and exploit knowledge with no size restrictions, with polynomial complexity.
A simplified relational framework is described; within this framework, one basic learning component, the generalization operator, is reconsidered.
Stochastic heuristics are combined with Plotkin's least general generalization to derive a stochastic generalization operator and a simple stochastic similarity function with controllable complexity. Preliminary experiments on the well-studied mutagenesis problem (regression-friendly and regression-unfriendly datasets) demonstrate the potential and the limitations of this similarity.
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Sebag, M. (1998). A stochastic simple similarity. In: Page, D. (eds) Inductive Logic Programming. ILP 1998. Lecture Notes in Computer Science, vol 1446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0027313
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DOI: https://doi.org/10.1007/BFb0027313
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