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
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Learning regular sets from queries and counterexamples
Information and Computation
Learning regular languages from counterexamples
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Learning read-once formulas with queries
Journal of the ACM (JACM)
Language learning with some negative information
Journal of Computer and System Sciences
Machine Learning
Machine Learning
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Learning A Class of Regular Expressions via Restricted Subset Queries
AII '92 Proceedings of the International Workshop on Analogical and Inductive Inference
Learning Erasing Pattern Languages with Queries
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Learning elementary formal systems with queries
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Learning languages from positive data and a finite number of queries
FSTTCS'04 Proceedings of the 24th international conference on Foundations of Software Technology and Theoretical Computer Science
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
Gold-style and query learning under various constraints on the target class
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Learning languages from positive data and a finite number of queries
FSTTCS'04 Proceedings of the 24th international conference on Foundations of Software Technology and Theoretical Computer Science
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A computational model for learning languages in the limit from full positive data and a bounded number of queries to the teacher (oracle) is introduced and explored. Equivalence, superset, and subset queries are considered. If the answer is negative, the teacher may provide a counterexample. We consider several types of counterexamples: arbitrary, least counterexamples, and no counterexamples. A number of hierarchies based on the number of queries (answers) and types of answers/ counterexamples is established. Capabilities of learning with different types of queries are compared. In most cases, one or two queries of one type can sometimes do more than any bounded number of queries of another type. Still, surprisingly, a finite number of subset queries is sufficient to simulate the same number of equivalence queries when behaviourally correct learners do not receive counterexamples and may have unbounded number of errors in almost all conjectures.