Аннотация
In this work, we consider the extension of the Inductive Functional Logic Programming (IFLP) framework in order to learn functions
in an incremental way. In general, incremental learning is necessary when the number of examples is infinite, very large orpresented one by one. We have performed this extension in the FLIP system, an implementation of the IFLP framework. Severalexamples of programs which have been induced indicate that our extension pays off in practice. An experimental study of someparameters which affect this efficiency is performed and some applications for programming practice are illustrated, especiallysmall classification problems and data-mining of semi-structured data.
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