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
Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Language learning from texts: mindchanges, limited memory, and monotonicity
Information and Computation
On the impact of forgetting on learning machines
Journal of the ACM (JACM)
Incremental learning from positive data
Journal of Computer and System Sciences
Incremental concept learning for bounded data mining
Information and Computation
Inference of Reversible Languages
Journal of the ACM (JACM)
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
Learning indexed families of recursive languages from positive data: A survey
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
Learnability of automatic classes
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
Learnability of automatic classes
Journal of Computer and System Sciences
Automatic learners with feedback queries
Journal of Computer and System Sciences
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Automatic classes are classes of languages for which a finite automaton can decide whether a given element is in a set given by its index. The present work studies the learnability of automatic families by automatic learners which, in each round, output a hypothesis and update a long term memory, depending on the input datum, via an automatic function, that is, via a function whose graph is recognised by a finite automaton. Many variants of automatic learners are investigated: where the long term memory is restricted to be the just prior hypothesis whenever this exists, cannot be of size larger than the size of the longest example or has to consist of a constant number of examples seen so far. Furthermore, learnability is also studied with respect to queries which reveal information about past data or past computation history; the number of queries per round is bounded by a constant. These models are generalisations of the model of feedback queries, given by Lange, Wiehagen and Zeugmann.