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Sequences Classification by Least General Generalisations
Abstract : 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 ork-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.
Cited literature [1 references]
https://hal.inria.fr/inria-00524707
Contributor : Fabien Torre <>
Submitted on : Friday, October 8, 2010 - 3:55:26 PM
Last modification on : Monday, January 13, 2020 - 5:46:02 PM
Document(s) archivé(s) le : Thursday, October 25, 2012 - 4:45:17 PM
Contributor : Fabien Torre <>
Submitted on : Friday, October 8, 2010 - 3:55:26 PM
Last modification on : Monday, January 13, 2020 - 5:46:02 PM
Document(s) archivé(s) le : Thursday, October 25, 2012 - 4:45:17 PM
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GloBallICGI2010.pdf
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