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
Teachability in computational learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Polynomial-time inference of arbitrary pattern languages
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Inductive inference from all positive and some negative data
Information Processing Letters
Types of monotonic language learning and their characterization
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On the power of inductive inference from good examples
Theoretical Computer Science
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Language Learning With Some Negative Information
STACS '93 Proceedings of the 10th Annual Symposium on Theoretical Aspects of Computer Science
Inductive Inference from Good Examples
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Formal languages and their relation to automata
Formal languages and their relation to automata
On the learnability of recursively enumerable languages from good examples
Theoretical Computer Science
Learnability of Enumerable Classes of Recursive Functions from "Typical" Examples
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
On the data consumption benefits of accepting increased uncertainty
Theoretical Computer Science
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Models of Cooperative Teaching and Learning
The Journal of Machine Learning Research
Inductive inference and language learning
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
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We study learning of indexable families of recursive languages from good examples. We show that this approach can be considerably more powerful than learning from all examples and point out reasons for this additional power. We present several characterizations of types of learning from good examples. We derive similarities as well as differences to learning of recursive functions from good examples.