Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Journal of the ACM (JACM)
On the structure of degrees of inferability
Journal of Computer and System Sciences
Machine Learning
Machine Learning
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Formal language identification: query learning vs. gold-style learning
Information Processing Letters
Relations between gold-style learning and query learning
Information and Computation
A general comparison of language learning from examples and from queries
Theoretical Computer Science
Iterative learning from positive data and negative counterexamples
Information and Computation
Learning languages from positive data and negative counterexamples
Journal of Computer and System Sciences
Journal of Computer and System Sciences
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Learning Balls of Strings with Correction Queries
ECML '07 Proceedings of the 18th European conference on Machine Learning
A characterization of the language classes learnable with correction queries
TAMC'07 Proceedings of the 4th 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
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As some cognitive research suggests, in the process of learning languages, in addition to overt explicit negative evidence, a child often receives covert explicit evidence in form of corrected or rephrased sentences. In this paper, we suggest one approach to formalization of overt and covert evidence within the framework of one-shot learners via subset and membership queries to a teacher (oracle). We compare and explore general capabilities of our models, as well as complexity advantages of learnability models of one type over models of other types, where complexity is measured in terms of number of queries. In particular, we establish that ''correcting'' positive examples are sometimes more helpful to a learner than just negative (counter) examples and access to full positive data.