Learning Pairwise Disjoint Simple Languages from Positive Examples
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 re...
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ftdatacite:10.48550/arxiv.1706.01663 2023-05-15T16:48:26+02:00 Learning Pairwise Disjoint Simple Languages from Positive Examples Linard, Alexis Smetsers, Rick Vaandrager, Frits Waqas, Umar van Pinxten, Joost Verwer, Sicco 2017 https://dx.doi.org/10.48550/arxiv.1706.01663 https://arxiv.org/abs/1706.01663 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Machine Learning cs.LG Formal Languages and Automata Theory cs.FL FOS Computer and information sciences Preprint Article article CreativeWork 2017 ftdatacite https://doi.org/10.48550/arxiv.1706.01663 2022-04-01T10:20:29Z 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. : This paper has been accepted at the Learning and Automata (LearnAut) Workshop, LICS 2017 (Reykjavik, Iceland) Report Iceland DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
unknown |
topic |
Machine Learning cs.LG Formal Languages and Automata Theory cs.FL FOS Computer and information sciences |
spellingShingle |
Machine Learning cs.LG Formal Languages and Automata Theory cs.FL FOS Computer and information sciences Linard, Alexis Smetsers, Rick Vaandrager, Frits Waqas, Umar van Pinxten, Joost Verwer, Sicco Learning Pairwise Disjoint Simple Languages from Positive Examples |
topic_facet |
Machine Learning cs.LG Formal Languages and Automata Theory cs.FL FOS Computer and information sciences |
description |
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. : This paper has been accepted at the Learning and Automata (LearnAut) Workshop, LICS 2017 (Reykjavik, Iceland) |
format |
Report |
author |
Linard, Alexis Smetsers, Rick Vaandrager, Frits Waqas, Umar van Pinxten, Joost Verwer, Sicco |
author_facet |
Linard, Alexis Smetsers, Rick Vaandrager, Frits Waqas, Umar van Pinxten, Joost Verwer, Sicco |
author_sort |
Linard, Alexis |
title |
Learning Pairwise Disjoint Simple Languages from Positive Examples |
title_short |
Learning Pairwise Disjoint Simple Languages from Positive Examples |
title_full |
Learning Pairwise Disjoint Simple Languages from Positive Examples |
title_fullStr |
Learning Pairwise Disjoint Simple Languages from Positive Examples |
title_full_unstemmed |
Learning Pairwise Disjoint Simple Languages from Positive Examples |
title_sort |
learning pairwise disjoint simple languages from positive examples |
publisher |
arXiv |
publishDate |
2017 |
url |
https://dx.doi.org/10.48550/arxiv.1706.01663 https://arxiv.org/abs/1706.01663 |
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Iceland |
genre_facet |
Iceland |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1706.01663 |
_version_ |
1766038528928514048 |