Abstract
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 some 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.
This work has been partially supported by CICYT under grant TIC 98-0445-C03-C1 and by Generalitat Valenciana under grant GV00-092-14.
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Ferri-Ramírez, C., Hernández-Orallo, J., Ramírez-Quintana, M. (2001). Incremental Learning of Functional Logic Programs. In: Kuchen, H., Ueda, K. (eds) Functional and Logic Programming. FLOPS 2001. Lecture Notes in Computer Science, vol 2024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44716-4_15
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