Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
The correct definition of finite elasticity: corrigendum to identification of unions
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
On the role of procrastination in machine learning
Information and Computation
The intrinsic complexity of language identification
Journal of Computer and System Sciences
Inference of Reversible Languages
Journal of the ACM (JACM)
Computable analysis: an introduction
Computable analysis: an introduction
Learning algebraic structures from text
Theoretical Computer Science - Algorithmic learning theory
Theoretical Computer Science
Characteristic Sets for Unions of Regular Pattern Languages and Compactness
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Unifying logic, topology and learning in parametric logic
Theoretical Computer Science - Algorithmic learning theory(ALT 2002)
Mind change efficient learning
Information and Computation
Inferability of closed set systems from positive data
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
Mind change complexity of inferring unbounded unions of pattern languages from positive data
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
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Based on the observation that the category of concept spaces with the positive information topology is equivalent to the category of countably based T0topological spaces, we investigate further connections between the learning in the limit model of inductive inference and topology. In particular, we show that the "texts" or "positive presentations" of concepts in inductive inference can be viewed as special cases of the "admissible representations" of computable analysis. We also show that several structural properties of concept spaces have well known topological equivalents. In addition to topological methods, we use algebraic closure operators to analyze the structure of concept spaces, and we show the connection between these two approaches. The goal of this paper is not only to introduce new perspectives to learning theorists, but also to present the field of inductive inference in a way more accessible to domain theorists and topologists.