The acquisition of syntactic knowledge
The acquisition of syntactic knowledge
Recursively enumerable sets and degrees
Recursively enumerable sets and degrees
Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Inductive inference of monotonic formal systems from positive data
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Learning elementary formal systems
Theoretical Computer Science
Computational learning of languages
Computational learning of languages
Subrecursive programming systems: complexity & succinctness
Subrecursive programming systems: complexity & succinctness
Characterizations of monotonic and dual monotonic language learning
Information and Computation
Monotonic and dual monotonic language learning
Theoretical Computer Science
Synthesizing enumeration techniques for language learning
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Angluin's theorem for indexed families of r.e. sets and applications
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
The Power of Vacillation in Language Learning
SIAM Journal on Computing
Vacillatory and BC learning on noisy data
Theoretical Computer Science - Special issue on algorithmic learning theory
Synthesizing noise-tolerant language learners
Theoretical Computer Science
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
Language Learning without Overgeneralization
STACS '92 Proceedings of the 9th Annual Symposium on Theoretical Aspects of Computer Science
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Characterization of Finite Identification
AII '92 Proceedings of the International Workshop on Analogical and Inductive Inference
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Robust separations in inductive inference
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Synthesizing inductive expertise
Information and Computation
On the Strength of Incremental Learning
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Refutable Language Learning with a Neighbor System
ALT '01 Proceedings of the 12th International Conference 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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An index for an r.e. class of languages (by definition) generates a sequence of grammars defining the class. An index for an indexed family of languages (by definition) generates a sequence of decision procedures defining the family. F. Stephan's model of noisy data is employed, in which, roughly, correct data crops up infinitely often, and incorrect data only finitely often. In a completely computable universe, all data sequences, even noisy ones, are computable. New to the present paper is the restriction that noisy data sequences be, nonetheless, computable! Studied, then, is the synthesis from indices for r.e. classes and for indexed families of languages of various kinds of noise-tolerant language-learners for the corresponding classes or families indexed, where the noisy input data sequences are restricted to being computable. Many positive results, as well as some negative results, are presented regarding the existence of such synthesizers. The main positive result is surprisingly more positive than its analog in the case the noisy data is not restricted to being computable: grammars for each indexed family can be learned behaviorally correctly from computable, noisy, positive data! The proof of another positive synthesis result yields, as a pleasant corollary, a strict subset-principle or telltale style characterization, for the computable noise-tolerant behaviorally correct learnability of grammars from positive and negative data, of the corresponding families indexed.