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
International Workshop All '86 on Analogical and inductive inference
Prudence and other conditions on formal language learning
Information and Computation
Language learning from texts: mindchanges, limited memory, and monotonicity
Information and Computation
Generalized notions of mind change complexity
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Ordinal mind change complexity of language identification
Theoretical Computer Science
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
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
On the Uniform Learnability of Approximations to Non-Recursive Functions
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Research in the theory of inductive inference by GDR mathematicians-A survey
Information Sciences: an International Journal
Theoretical Computer Science - Special issue: Algorithmic learning theory
| Hi-index | 0.00 |
The main question addressed in the present work is how to findeffectiv ely a recursive function separating two sets drawn arbitrarily from a given collection of disjoint sets. In particular, it is investigated in which cases it is possible to satisfy the following additional constraints: confidence where the learner converges on all data-sequences; conservativeness where the learner abandons only definitely wrong hypotheses; consistency where also every intermediate hypothesis is consistent with the data seen so far; set-driven learners whose hypotheses are independent of the order and the number of repetitions of the data-items supplied; learners where either the last or even all hypotheses are programs of total recursive functions.The present work gives an overview of the relations between these notions and succeeds to answer many questions by finding ways to carry over the corresponding results from other scenarios within inductive inference. Nevertheless, the relations between conservativeness and setdriven inference needed a novel approach which enabled to show the following two major results: (1) There is a class for which recursive separators can be foundin a confident and set-driven way, but no conservative learner finds a (not necessarily total) separator for this class. (2) There is a class for which recursive separators can be foundin a confident and conservative way, but no set-driven learner finds a (not necessarily total) separator for this class.