Abstract
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 different 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 definition 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.
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Markov, Z., Marinchev, I. (2000). Metric-Based Inductive Learning Using Semantic Height Functions. In: López de Mántaras, R., Plaza, E. (eds) Machine Learning: ECML 2000. ECML 2000. Lecture Notes in Computer Science(), vol 1810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45164-1_27
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DOI: https://doi.org/10.1007/3-540-45164-1_27
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