Systems that learn: an introduction to learning theory for cognitive and computer scientists
Systems that learn: an introduction to learning theory for cognitive and computer scientists
Learning regular sets from queries and counterexamples
Information and Computation
Polynomial-time inference of arbitrary pattern languages
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Rich classes inferable from positive data
Information and Computation
On the impact of forgetting on learning machines
Journal of the ACM (JACM)
Inference of Reversible Languages
Journal of the ACM (JACM)
Locality, Reversibility, and Beyond: Learning Languages from Positive Data
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Automatic Presentations of Structures
LCC '94 Selected Papers from the International Workshop on Logical and Computational Complexity
Identification of function distinguishable languages
Theoretical Computer Science
LICS '00 Proceedings of the 15th Annual IEEE Symposium on Logic in Computer Science
Uncountable automatic classes and learning
Theoretical Computer Science
Automatic learning of subclasses of pattern languages
LATA'11 Proceedings of the 5th international conference on Language and automata theory and applications
Automatic learners with feedback queries
CiE'11 Proceedings of the 7th conference on Models of computation in context: computability in Europe
Robust learning of automatic classes of languages
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Automatic functions, linear time and learning
CiE'12 Proceedings of the 8th Turing Centenary conference on Computability in Europe: how the world computes
Automatic learning of subclasses of pattern languages
Information and Computation
Automatic learning from positive data and negative counterexamples
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
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The present work initiates the study of the learnability of automatic indexable classes which are classes of regular languages of a certain form. Angluin's tell-tale condition characterizes when these classes are explanatorily learnable. Therefore, the more interesting question is when learnability holds for learners with complexity bounds, formulated in the automata-theoretic setting. The learners in question work iteratively, in some cases with an additional long-term memory, where the update function of the learner mapping old hypothesis, old memory and current datum to new hypothesis and new memory is automatic. Furthermore, the dependence of the learnability on the indexing is also investigated. This work brings together the fields of inductive inference and automatic structures.