Inference Improvement by Enlarging the Training Set While Learning DFAs
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.
KeywordsRegular Expression Target Language Regular Language Inclusion Relation Grammatical Inference
- 8.Nicaud, C.: Etude du comportment des automates finis et des langages rationnels. Ph.D. Thesis, Université de Marne la Vallée (2001)Google Scholar
- 9.Oncina, J., García, P.: Inferring Regular Languages in Polynomial Updated Time. In: de la Blanca, P., Sanfeliú, Vidal (eds.) Pattern Recognition and Image Analysis. World Scientific, Singapore (1992)Google Scholar