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
A connotational theory of program structure
A connotational theory of program structure
Prudence and other conditions on formal language learning
Information and Computation
Monotonic and non-monotonic inductive inference
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Learning with the knowledge of an upper bound on program size
Information and Computation
Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Subrecursive programming systems: complexity & succinctness
Subrecursive programming systems: complexity & succinctness
Characterizations of monotonic and dual monotonic language learning
Information and Computation
Monotonic and dual monotonic language learning
Theoretical Computer Science
Incremental learning from positive data
Journal of Computer and System Sciences
The Power of Vacillation in Language Learning
SIAM Journal on Computing
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Control structures in hypothesis spaces: the influence on learning
Theoretical Computer Science
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
A Thesis in Inductive Inference
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
A general comparison of language learning from examples and from queries
Theoretical Computer Science
Information and Computation
Learning recursive functions: A survey
Theoretical Computer Science
Learning in Friedberg numberings
Information and Computation
Prescribed Learning of Indexed Families
Fundamenta Informaticae
Numberings Optimal for Learning
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Prescribed learning of r.e. classes
Theoretical Computer Science
| Hi-index | 0.00 |
In this paper we survey some results in inductive inference showing how learnability of a class of languages may depend on the hypothesis space chosen. Additionally, optimal hypothesis spaces, usable for every learnable class, are considered. We also discuss results which consider how learnability is effected if one requires learning using every suitable hypothesis space.