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
The position of index sets of identifiable sets in the arithmetical hierarchy
Information and Control
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Towards the development of an analysis of learning algorithms
International Workshop All '86 on Analogical and inductive inference
On the inference of programs approximately computing the desired function
International Workshop All '86 on Analogical and inductive inference
International Workshop All '86 on Analogical and inductive inference
Saving the phenomena: requirements that inductive inference machines not contradict known data
Information and Computation
Probabilistic inductive inference
Journal of the ACM (JACM)
Trade-off among parameters affecting inductive inference
Information and Computation
Inductive inference of minimal programs
COLT '90 Proceedings of the third annual workshop on Computational learning theory
On the role of search for learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
One-sided error probabilistic inductive inference and reliable frequency identification
Information and Computation
Equivalence of models for polynomial learnability
Information and Computation
On the necessity of Occam algorithms
Theoretical Computer Science
On the power of inductive inference from good examples
Theoretical Computer Science
Learning with the knowledge of an upper bound on program size
Information and Computation
Learning programs with an easy to calculate set of errors
Fundamenta Informaticae
The weighted majority algorithm
Information and Computation
Choosing a learning team: a topological approach
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
A recursive introduction to the theory of computation
A recursive introduction to the theory of computation
The nature of statistical learning theory
The nature of statistical learning theory
Learning Binary Relations Using Weighted Majority Voting
Machine Learning
On the intrinsic complexity of learning
Information and Computation
Asking questions to minimize errors
Journal of Computer and System Sciences
Incremental learning from positive data
Journal of Computer and System Sciences
On the intrinsic complexity of learning recursive functions
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Incremental concept learning for bounded data mining
Information and Computation
Robust behaviorally correct learning
Information and Computation
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Computational Complexity and the Existence of Complexity Gaps
Journal of the ACM (JACM)
Journal of the ACM (JACM)
Recursive Properties of Abstract Complexity Classes
Journal of the ACM (JACM)
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
Robust learning aided by context
Journal of Computer and System Sciences - Eleventh annual conference on computational learning theory&slash;Twelfth Annual IEEE conference on computational complexity
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Journal of Computer and System Sciences
Probabilistic inductive inference: a survey
Theoretical Computer Science
Closedness properties in ex-identification
Theoretical Computer Science - Algorithmic learning theory
Machine Learning
Identifying nearly minimal Gödel numbers from additional information
Annals of Mathematics and Artificial Intelligence
Learning classes of approximations to non-recursive functions
Theoretical Computer Science
Collaborative Filtering Using Weighted Majority Prediction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Characterization Problems in the Theory of Inductive Inference
Proceedings of the Fifth Colloquium on Automata, Languages and Programming
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
A Refutation of Barzdins' Conjecture
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Three Decades of Team Learning
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
Transformations that Preserve Learnability
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
A Thesis in Inductive Inference
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
Inductive Inference of Optimal Programs: A Survey and Open Problems
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
Inductive Inference of Recursive Functions: Complexity Bounds
Baltic Computer Science, Selected Papers
Classes of computable functions defined by bounds on computation: Preliminary Report
STOC '69 Proceedings of the first annual ACM symposium on Theory of computing
On the intrinsic complexity of learning recursive functions
Information and Computation
Tradeoffs in machine inductive inference
Tradeoffs in machine inductive inference
Separation of uniform learning classes
Theoretical Computer Science - Special issue: Algorithmic learning theory
Robust learning: rich and poor
Journal of Computer and System Sciences
An approach to intrinsic complexity of uniform learning
Theoretical Computer Science - Algorithmic learning theory
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Reflective inductive inference of recursive functions
Theoretical Computer Science
Robust separations in inductive inference
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Inductive learning from good examples
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Research in the theory of inductive inference by GDR mathematicians-A survey
Information Sciences: an International Journal
Consistency conditions for inductive inference of recursive functions
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
On some open problems in reflective inductive inference
Information Processing Letters
Hypothesis Spaces for Learning
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
An introduction to inductive programming
Artificial Intelligence Review
Hypothesis spaces for learning
Information and Computation
On the amount of nonconstructivity in learning recursive functions
TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Confident and consistent partial learning of recursive functions
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Hi-index | 5.23 |
Studying the learnability of classes of recursive functions has attracted considerable interest for at least four decades. Starting with Gold's (1967) model of learning in the limit, many variations, modifications and extensions have been proposed. These models differ in some of the following: the mode of convergence, the requirements intermediate hypotheses have to fulfill, the set of allowed learning strategies, the source of information available to the learner during the learning process, the set of admissible hypothesis spaces, and the learning goals. A considerable amount of work done in this field has been devoted to the characterization of function classes that can be learned in a given model, the influence of natural, intuitive postulates on the resulting learning power, the incorporation of randomness into the learning process, the complexity of learning, among others. On the occasion of Rolf Wiehagen's 60th birthday, the last four decades of research in that area are surveyed, with a special focus on Rolf Wiehagen's work, which has made him one of the most influential scientists in the theory of learning recursive functions.