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
Recursively enumerable sets and degrees
Recursively enumerable sets and degrees
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Learning regular sets from queries and counterexamples
Information and Computation
Handbook of theoretical computer science (vol. B)
Journal of the ACM (JACM)
Hilbert's tenth problem
Regular Article: Open problems in “systems that learn”
Proceedings of the 30th IEEE symposium on Foundations of computer science
On the impact of forgetting on learning machines
Journal of the ACM (JACM)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Incremental concept learning for bounded data mining
Information and Computation
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Machine Learning
Machine Learning
Three Decades of Team Learning
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
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This work introduces a new query inference model that can access data and communicate with the teacher by asking finitely many Boolean queries in a language L. In this model the parameters of interest are the number of queries used and the expressive power of L. We study how the learning power varies with these parameters. Results suggest that this model may help studying query inference in a resource bounded environment.