A characterization of the language classes learnable with correction queries

Authors:
Cristina Tîrnăucă;Satoshi Kobayashi
Affiliations:
Research Group on Mathematical Linguistics, Rovira i Virgili University, Tarragona, Spain;Department of Computer Science, University of Electro-Communications, Chofu, Tokyo, Japan
Venue:
TAMC'07 Proceedings of the 4th international conference on Theory and applications of models of computation
Year:
2007

Citing 8
Cited 4

Learning regular sets from queries and counterexamples

Information and Computation
Types of monotonic language learning and their characterization

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Characterizations of monotonic and dual monotonic language learning

Information and Computation
A Guided Tour Across the Boundaries of Learning Recursive Languages

Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Characterization of Finite Identification

AII '92 Proceedings of the International Workshop on Analogical and Inductive Inference
Formal language identification: query learning vs. gold-style learning

Information Processing Letters
Inductive inference and language learning

TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
Learning DFA from correction and equivalence queries

ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications

Polynomial Time Algorithms for Learning k-Reversible Languages and Pattern Languages with Correction Queries

ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
A Note on the Relationship between Different Types of Correction Queries

ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
One-shot learners using negative counterexamples and nearest positive examples

Theoretical Computer Science
MAT learners for recognizable tree languages and tree series

Acta Cybernetica

Quantified Score

Hi-index	0.00

Visualization

Abstract

Formal language learning models have been widely investigated in the last four decades. But it was not until recently that the model of learning from corrections was introduced. The aim of this paper is to make a further step towards the understanding of the classes of languages learnable with correction queries. We characterize these classes in terms of triples of definite finite tell-tales. This result allowed us to show that learning with correction queries is strictly more powerful than learning with membership queries, but weaker than the model of learning in the limit from positive data.