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
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Learning regular sets from queries and counterexamples
Information and Computation
Monotonic and non-monotonic inductive inference
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Polynomial-time inference of arbitrary pattern languages
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Subrecursive programming systems: complexity & succinctness
Subrecursive programming systems: complexity & succinctness
Incremental concept learning for bounded data mining
Information and Computation
Identification of function distinguishable languages
Theoretical Computer Science
Inferability of closed set systems from positive data
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
Grammatical Inference: Learning Automata and Grammars
Grammatical Inference: Learning Automata and Grammars
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Formal and empirical grammatical inference
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Tier-based strictly local constraints for phonology
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Learning families of closed sets in matroids
WTCS'12 Proceedings of the 2012 international conference on Theoretical Computer Science: computation, physics and beyond
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The class of regular languages is not identifiable from positive data in Gold's language learning model. Many attempts have been made to define interesting classes that are learnable in this model, preferably with the associated learner having certain advantageous properties. Heinz '09 presents a set of language classes called String Extension (Learning) Classes, and shows it to have several desirable properties. In the present paper, we extend the notion of String Extension Classes by basing it on lattices and formally establish further useful properties resulting from this extension. Using lattices enables us to cover a larger range of language classes including the pattern languages, as well as to give various ways of characterizing String Extension Classes and its learners. We believe this paper to show that String Extension Classes are learnable in a very natural way, and thus worthy of further study.