Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
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
The synthesis of language learners
Information and Computation
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
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
A Negative Result on Inductive Inference of Extended Pattern Languages
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Theoretical Computer Science - Special issue: Algorithmic learning theory
Formal language identification: query learning vs. gold-style learning
Information Processing Letters
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
A general comparison of language learning from examples and from queries
Theoretical Computer Science
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Discontinuities in pattern inference
Theoretical Computer Science
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
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Different formal learning models address different aspects of human learning. Below we compare Gold-style learning--modelling learning as a limiting process in which the learner may change its mind arbitrarily often before converging to a correct hypothesis to learning via queries modelling learning as a one-shot process in which the learner is required to identify the target concept with just one hypothesis. In the Gold-style model considered below, the information presented to the learner consists of positive examples for the target concept, whereas in query learning, the learner may pose a certain kind of queries about the target concept, which will be answered correctly by an oracle (called teacher). Although these two approaches seem rather unrelated at first glance, we provide characterisations of different models of Gold-style learning (learning in the limit, conservative inference, and behaviourally correct learning) in terms of query learning. Thus, we describe the circumstances which are necessary to replace limit learners by equally powerful one-shot learners. Our results are valid in the general context of learning indexable classes of recursive languages. This analysis leads to an important observation, namely that there is a natural query learning type hierarchically in-between Gold-style learning in the limit and behaviourally correct learning. Astonishingly, this query learning type can then again be characterised in terms of Gold-style inference.