Incremental Regular Inference (1996)
| Venue: | Proceedings of the Third ICGI-96 |
| Citations: | 26 - 1 self |
BibTeX
@INPROCEEDINGS{Dupont96incrementalregular,
author = {Pierre Dupont},
title = {Incremental Regular Inference},
booktitle = {Proceedings of the Third ICGI-96},
year = {1996},
pages = {222--237},
publisher = {Springer}
}
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Abstract
In this paper, we extend the characterization of the search space of regular inference [DMV94] to sequential presentations of learning data. We propose the RPNI2 algorithm, an incremental extension of the RPNI algorithm. We study the convergence and complexities of both algorithms from a theoretical and practical point of view. These results are assessed on the Feldman task. 1 Introduction Regular inference is the problem of learning a regular language from a positive sample, that is, a finite set of strings supposed to be drawn from a target language. Whenever a negative sample, that is, a finite set of strings not belonging to the target language, is also available, the problem may be solved by the RPNI algorithm 1 proposed by Oncina and Garc'ia [OG92] and, independently, by Lang [Lan92]. The RPNI algorithm has been shown to identify in the limit any regular language with polynomial complexity as a function of the positive and negative sample sizes. However, this algorithm requir...
