Incremental Learning of Functional Logic Programs [chapter]

C. Ferri-Ramírez, J. Hernández-Orallo, M.J. Ramírez-Quintana
<span title="">2001</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
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 or presented one by one. We have performed this extension in the FLIP system, an implementation of the IFLP framework. Several examples of programs which have been induced indicate that our extension pays off in practice. An experimental study of
more &raquo; ... ome parameters which affect this efficiency is performed and some applications for programming practice are illustrated, especially small classification problems and data-mining of semi-structured data.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-44716-4_15">doi:10.1007/3-540-44716-4_15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6d5dorwvynavlerrff3yhin6t4">fatcat:6d5dorwvynavlerrff3yhin6t4</a> </span>
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