Communications of the ACM
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Computational limitations on learning from examples
Journal of the ACM (JACM)
Prudence and other conditions on formal language learning
Information and Computation
Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
An introduction to computational learning theory
An introduction to computational learning theory
Randomized algorithms
Angluin's theorem for indexed families of r.e. sets and applications
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
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
Theoretical Computer Science - Special issue: Algorithmic learning theory
Relations between gold-style learning and query learning
Information and Computation
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
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
Hypothesis Spaces for Learning
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
One-shot learners using negative counterexamples and nearest positive examples
Theoretical Computer Science
Necessary and sufficient conditions for learning with correction queries
Theoretical Computer Science
Hypothesis spaces for learning
Information and Computation
| Hi-index | 5.23 |
In language learning, strong relationships between Gold-style models and query models have recently been observed: in some quite general setting Gold-style learners can be replaced by query learners and vice versa, without loss of learning capabilities. These 'equalities' hold in the context of learning indexable classes of recursive languages. Former studies on Gold-style learning of such indexable classes have shown that, in many settings, the enumerability of the target class and the recursiveness of its languages are crucial for learnability. Moreover, studying query learning, non-indexable classes have been mainly neglected up to now. So it is conceivable that the recently observed relations between Gold-style and query learning are not due to common structures in the learning processes in both models, but rather to the enumerability of the target classes or the recursiveness of their languages. In this paper, the analysis is lifted onto the context of learning arbitrary classes of recursively enumerable languages. Still, strong relationships between the approaches of Gold-style and query learning are proven, but there are significant changes to the former results. Though in many cases learners of one type can still be replaced by learners of the other type, in general this does not remain valid vice versa. All results hold even for learning classes of recursive languages, which indicates that the recursiveness of the languages is not crucial for the former 'equality' results. Thus we analyze how constraints on the algorithmic structure of the target class affect the relations between two approaches to language learning.