Communications of the ACM
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
Learning regular sets from queries and counterexamples
Information and Computation
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Polynomial-time inference of arbitrary pattern languages
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Computational learning theory: an introduction
Computational learning theory: an introduction
Rich classes inferable from positive data
Information and Computation
Subrecursive programming systems: complexity & succinctness
Subrecursive programming systems: complexity & succinctness
Theoretical Computer Science
Incremental concept learning for bounded data mining
Information and Computation
Journal of the ACM (JACM)
Inference of Reversible Languages
Journal of the ACM (JACM)
Automata, Languages, and Machines
Automata, Languages, and Machines
Automata, Languages, and Machines
Automata, Languages, and Machines
Machine Learning
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 2nd GI Conference on Automata Theory and Formal Languages
On Approximately Identifying Concept Classes in the Limit
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Monotonic and Nonmonotonic Inductive Inference of Functions and Patterns
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
Monotonic Versus Nonmonotonic Language Learning
Proceedings of the Second International Workshop on Nonmonotonic and Inductive Logic
Polynomial-time identification of very simple grammars from positive data
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Identification of function distinguishable languages
Theoretical Computer Science
Some natural conditions on incremental learning
Information and Computation
Polynomial Identification in the Limit of Substitutable Context-free Languages
The Journal of Machine Learning Research
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Duality and Equational Theory of Regular Languages
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part II
Inferability of closed set systems from positive data
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
Difficulties in forcing fairness of polynomial time inductive inference
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
On languages piecewise testable in the strict sense
MOL'07/09 Proceedings of the 10th and 11th Biennial conference on The mathematics of language
Aural Pattern Recognition Experiments and the Subregular Hierarchy
Journal of Logic, Language and Information
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We define a collection of language classes which are TxtEx-learnable (learnable in the limit from positive data). The learners map any data input to an element of a fixed lattice, and keep the least upper bound of all lattice elements thus obtained as the current hypothesis. Each element of the lattice is a grammar for a language, and the learner climbs the lattice searching for the right element. We call these classes in our collection lattice classes. We provide several characterizations of lattice classes and their learners, which suggests they are very natural. In particular, we show that any class of languages is a lattice class iff it is TxtEx-learnable consistently, conservatively, set-drivenly, and strongly monotonically. We show several language classes previously discussed in the literature to be lattice classes, including the locally k-testable classes, the piecewise k-testable classes, the k-reversible languages and the pattern languages. We also show that lattice classes contain three previously known collections of language classes: string extension language classes, function-distinguishable language classes, and closed-set systems. Finally, the lattice perspective helps analyze the learning of these classes. Illustrations include query-learning results in dependence on the lattice structure, characterizations of closure properties and the VC-dimension of lattice classes in terms of lattice properties.