Metric-Based Inductive Learning Using Semantic Height Functions

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Metric-Based Inductive Learning Using Semantic Height Functions
In the present paper we propose a consistent way to integrate syntactical least general generalizations (lgg's) with semantic evaluation of the hypotheses. For this purpose we use two di erent relations on the hypothesis space { a constructive one, used to generate lgg's and a semantic one giving the coverage-based evaluation of the lgg. These two relations jointly implement a semantic distance measure. The formal background for this is a height-based de nition of a semi-distance in a join semi-lattice. We use some basic results from lattice theory and introduce a family of language independent coverage-based height functions. The theoretical results are illustrated by examples of solving some basic inductive learning tasks.
Zdravko Markov, Ivo Marinchev
Added 02 Aug 2010
Updated 02 Aug 2010
Type Conference
Year 2000
Where ECML
Authors Zdravko Markov, Ivo Marinchev
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