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
Learning regular sets from queries and counterexamples
Information and Computation
Prudence and other conditions on formal language learning
Information and Computation
Learning via queries to an oracle
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Higher recursion theory
Inductive inference from all positive and some negative data
Information Processing Letters
On the non-existence of maximal inference degrees for language identification
Information Processing Letters
On the role of procrastination in machine learning
Information and Computation
Language learning with some negative information
Journal of Computer and System Sciences
The Power of Vacillation in Language Learning
SIAM Journal on Computing
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
Not-So-Nearly-Minimal-Size Program Inference
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Classes with Easily Learnable Subclasses
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
On the learnability of vector spaces
Journal of Computer and System Sciences
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The present work is dedicated to the study of modes of data-presentation in the range between text and informant within the framework of inductive inference. In this study, the learner alternatingly requests sequences of positive and negative data. We define various formalizations of valid data presentations in such a scenario. We resolve the relationships between these different formalizations, and show that one of these is equivalent to learning from informant. We also show a hierarchy formed (for each of the formalizations studied) by considering the number of switches between requests for positive and negative data.