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
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Types of monotonic language learning and their characterization
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Incremental learning from positive data
Journal of Computer and System Sciences
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Incremental concept learning for bounded data mining
Information and Computation
Machine Learning
Machine Learning
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
Iterative learning from positive data and negative counterexamples
Information and Computation
Learning languages from positive data and negative counterexamples
Journal of Computer and System Sciences
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
U-shaped, iterative, and iterative-with-counter learning
Machine Learning
Solutions to open questions for non-u-shaped learning with memory limitations
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Memory-limited non-U-shaped learning with solved open problems
Theoretical Computer Science
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A variant of iterative learning in the limit (cf. [LZ96]) is studied when a learner gets negative examples refuting conjectures containing data in excess of the target language and uses additional information of the following four types: a) memorizing up to n input elements seen so far; b) up to n feedback memberships queries (testing if an item is a member of the input seen so far); c) the number of input elements seen so far; d) the maximal element of the input seen so far. We explore how additional information available to such learners (defined and studied in [JK07]) may help. In particular, we show that adding the maximal element or the number of elements seen so far helps such learners to infer any indexed class of languages class-preservingly (using a descriptive numbering defining the class) -- as it is proved in [JK07], this is not possible without using additional information. We also study how, in the given context, different types of additional information fare against each other, and establish hierarchies of learners memorizing n + 1 versus n input elements seen and n +1 versus n feedback membership queries.