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
Prudence and other conditions on formal language learning
Information and Computation
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Learning automata from ordered examples
COLT '88 Proceedings of the first annual workshop on Computational learning theory
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
Computational learning of languages
Computational learning of languages
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
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
Inductive Inference from Good Examples
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Monotonic and Nonmonotonic Inductive Inference of Functions and Patterns
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
A Thesis in Inductive Inference
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
Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
The representation of recursive languages and its impact on the efficiency of learning
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Language learning from texts (extended abstract): mind changes, limited memory and monotonicity
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Generalized notions of mind change complexity
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Aspects of complexity of probabilistic learning under monotonicity constraints
Theoretical Computer Science - Algorithmic learning theory
Generalization and specialization strategies for learning r.e. languages
Annals of Mathematics and Artificial Intelligence
Learning recursive languages from good examples
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence
Learning Recursive Concepts with Anomalies
ALT '00 Proceedings of the 11th 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
Inferring a Rewriting System from Examples
DS '98 Proceedings of the First International Conference on Discovery Science
On Variants of Iterative Learning
DS '98 Proceedings of the First International Conference on Discovery Science
Language Learning with a Neighbor System
DS '00 Proceedings of the Third International Conference on Discovery Science
Variants of iterative learning
Theoretical Computer Science
Refutable language learning with a neighbor system
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Inductive inference of approximations for recursive concepts
Theoretical Computer Science - Algorithmic learning theory (ALT 2000)
Iterative learning from positive data and negative counterexamples
Information and Computation
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Algorithms for learning regular expressions from positive data
Information and Computation
On some open problems in monotonic and conservative learning
Information Processing Letters
Necessary and sufficient conditions for learning with correction queries
Theoretical Computer Science
A characterization of the language classes learnable with correction queries
TAMC'07 Proceedings of the 4th international conference on Theory and applications of models of computation
Iterative learning from texts and counterexamples using additional information
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Finite identification from the viewpoint of epistemic update
Information and Computation
Learning with ordinal-bounded memory from positive data
Journal of Computer and System Sciences
On the amount of nonconstructivity in learning formal languages from positive data
TAMC'12 Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation
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The present paper deals with strong-monotonic, monotonic and weak-monotonic language learning from positive data as well as from positive and negative examples. The three notions of monotonicity reflect different formalizations of the requirement that the learner has to produce always better and better generalizations when fed more and more data on the concept to be learnt. We characterize strong-monotonic, monotonic, weak-monotonic and finite language learning from positive data in terms of recursively generable finite sets, thereby solving a problem of Angluin (1980). Moreover, we study monotonic inference with iteratively working learning devices which are of special interest in applications. In particular, it is proved that strong-monotonic inference can be performed with iteratively learning devices without limiting the inference capabilities, while monotonic and weak-monotonic inference cannot.