Abstract
A new version of the RPNI algorithm, called RPNI2, is presented. The main difference between them is the capability of the new one to extend the training set during the inference process. The effect of this new feature is specially notorious in the inference of languages generated from regular expressions and Non-deterministic Finite Automata (NFA). A first experimental comparison is done between RPNI2 and DeLeTe2, other algorithm that behaves well with the same sort of training data.1 © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
García, P., Ruiz, J., Cano, A., & Alvarez, G. (2005). Inference improvement by enlarging the training set while learning DFAs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3773 LNCS, pp. 59–70). Springer Verlag. https://doi.org/10.1007/11578079_7
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
