Active Learning of Sequential Transducers with Side Information About the Domain
Active learning is a setting in which a student queries a teacher, through membership and equivalence queries, in order to learn a language. Performance on these algorithms is often measured in the number of queries required to learn a target, with an emphasis on costly equivalence queries. In grayb...
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Published in | Developments in Language Theory pp. 54 - 65 |
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Main Authors | , , , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
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Series | Lecture Notes in Computer Science |
Online Access | Get full text |
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Summary: | Active learning is a setting in which a student queries a teacher, through membership and equivalence queries, in order to learn a language. Performance on these algorithms is often measured in the number of queries required to learn a target, with an emphasis on costly equivalence queries. In graybox learning, the learning process is accelerated by foreknowledge of some information on the target. Here, we consider graybox active learning of subsequential string transducers, where a regular overapproximation of the domain is known by the student. We show that there exists an algorithm to learn subsequential string transducers with a better guarantee on the required number of equivalence queries than classical active learning. |
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Bibliography: | This work was supported by the ARC “Non-Zero Sum Game Graphs” project (Fédération Wallonie-Bruxelles), the EOS “Verilearn” project (F.R.S.-FNRS & FWO), and the FWO “SAILor” project (G030020N). |
ISBN: | 3030815072 9783030815073 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-030-81508-0_5 |