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
Can finite samples detect singularities of real-valued functions?
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Towards a mathematical theory of machine discovery from facts
Theoretical Computer Science - Special issue on algorithmic learning theory
Learning approximately regular languages with reversible languages
Theoretical Computer Science
A note on batch and incremental learnability
Journal of Computer and System Sciences
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
Inductive Inference Machines That Can Refute Hypothesis Spaces
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Machine Discovery in the Presence of Incomplete or Ambiguous Data
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
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
Reflecting and Self-Confident Inductive Inference Machines
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
On Approximately Identifying Concept Classes in the Limit
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Reflecting Inductive Inference Machines and Its Improvement by Therapy
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Language Learning with a Neighbor System
DS '00 Proceedings of the Third International Conference on Discovery Science
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In their pioneering work, Mukouchi and Arikawa modeled a learning situation in which the learner is expected to refute texts which are not representative of L, the class of languages being identified. Lange and Watson extended this model to consider justified refutation in which the learner is expected to refute texts only if it contains a finite sample unrepresentative of the class L. Both the above studies were in the context of indexed families of recursive languages. We extend this study in two directions. Firstly, we consider general classes of recursively enumerable languages. Secondly, we allow the machine to either identify or refute the unrepresentative texts (respectively, texts containing finite unrepresentative samples). We observe some surprising differences between our results and the results obtained for learning indexed families by Lange and Watson.