| Autor: |
Tantini, Frédéric, Terlutte, Alain, Torre, Fabien |
| Zdroj: |
Grammatical Inference: Theoretical Results & Applications; 2010, p189-202, 14p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
