Metric-Based Inductive Learning Using Semantic Height Functions
نویسندگان
چکیده
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 for mal 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 func tions The theoretical results are illustrated by examples of solving some basic inductive learning tasks
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