Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Machine Learning
Machine Learning
Automatic Presentations of Structures
LCC '94 Selected Papers from the International Workshop on Logical and Computational Complexity
A Thesis in Inductive Inference
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
LICS '00 Proceedings of the 15th Annual IEEE Symposium on Logic in Computer Science
Learning languages from positive data and negative counterexamples
Journal of Computer and System Sciences
Learnability of automatic classes
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
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We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with a membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones whose size is bounded by the longest positive datum seen so far) can impact automatic learnability.