Tantini, Frédéric; Terlutte, Alain; Torre, Fabien Sequences classification by least general generalisations. (English) Zbl 1291.68207 Sempere, José M. (ed.) et al., Grammatical inference: Theoretical results and applications. 10th international colloquium, ICGI 2010, Valencia, Spain, September 13–16, 2010. Proceedings. Berlin: Springer (ISBN 978-3-642-15487-4/pbk). Lecture Notes in Computer Science 6339. Lecture Notes in Artificial Intelligence, 189-202 (2010). Summary: In this paper, we present a general framework for supervised classification. This framework provides methods like boosting and only needs the definition of a generalisation operator called lgg. For sequence classification tasks, lgg is a learner that only uses positive examples. We show that grammatical inference has already defined such learners for automata classes like reversible automata or \(k\)-TSS automata. Then we propose a generalisation algorithm for the class of balls of words. Finally, we show through experiments that our method efficiently resolves sequence classification tasks.For the entire collection see [Zbl 1195.68011]. MSC: 68Q32 Computational learning theory 68Q42 Grammars and rewriting systems 68Q45 Formal languages and automata 68T10 Pattern recognition, speech recognition Keywords:sequence classification; least general automata; balls of words PDFBibTeX XMLCite \textit{F. Tantini} et al., Lect. Notes Comput. Sci. 6339, 189--202 (2010; Zbl 1291.68207) Full Text: DOI Link