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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
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