Learning Pairwise Disjoint Simple Languages from Positive Examples
Machine Learning
2017-06-07 v1 Formal Languages and Automata Theory
Abstract
A classical problem in grammatical inference is to identify a deterministic finite automaton (DFA) from a set of positive and negative examples. In this paper, we address the related - yet seemingly novel - problem of identifying a set of DFAs from examples that belong to different unknown simple regular languages. We propose two methods based on compression for clustering the observed positive examples. We apply our methods to a set of print jobs submitted to large industrial printers.
Cite
@article{arxiv.1706.01663,
title = {Learning Pairwise Disjoint Simple Languages from Positive Examples},
author = {Alexis Linard and Rick Smetsers and Frits Vaandrager and Umar Waqas and Joost van Pinxten and Sicco Verwer},
journal= {arXiv preprint arXiv:1706.01663},
year = {2017}
}
Comments
This paper has been accepted at the Learning and Automata (LearnAut) Workshop, LICS 2017 (Reykjavik, Iceland)