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
A study of inductive inference machines
A study of inductive inference machines
Some results in the theory of effective program synthesis: learning by defective information
Proceedings of the International Spring School on Mathematical method of specification and synthesis of software systems '85
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Monotonic and non-monotonic inductive inference
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
On the impact of forgetting on learning machines
Journal of the ACM (JACM)
Learning in the presence of inaccurate information
Theoretical Computer Science
Learning approximately regular languages with reversible languages
Theoretical Computer Science
Theoretical Computer Science - Special issue on algorithmic learning theory
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
Generalization and specialization strategies for learning r.e. languages
Annals of Mathematics and Artificial Intelligence
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Inductive Inference of an Approximate Concept from Positive Data
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
On Approximately Identifying Concept Classes in the Limit
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Identifiability of Subspaces and Homomorphic Images of Zero-Reversible Languages
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
A Thesis in Inductive Inference
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
Learning all subfunctions of a function
Information and Computation
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Learning and extending sublanguages
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Inductive inference of languages from samplings
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Inferring descriptive generalisations of formal languages
Journal of Computer and System Sciences
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A number of natural models for learning in the limit are introduced to deal with the situation when a learner is required to provide a grammar covering the input even if only a part of the target language is available. Examples of language families are exhibited that are learnable in one model and not learnable in another one. Some characterizations for learnability of algorithmically enumerable families of languages for the models in question are obtained. Since learnability of any part of the target language does not imply monotonicity of the learning process, we consider our models also under the additional monotonicity constraint.