Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Towards a mathematical theory of machine discovery from facts
Theoretical Computer Science - Special issue on algorithmic learning theory
Journal of Computer and System Sciences - Fourteenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Machine Discovery in the Presence of Incomplete or Ambiguous Data
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
Refutably Probably Approximately Correct Learning
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
Reflecting and Self-Confident Inductive Inference Machines
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Reflecting Inductive Inference Machines and Its Improvement by Therapy
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Refutable Language Learning with a Neighbor System
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Learning Recursive Functions Refutably
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
One-sided error probabilistic inductive inference and reliable frequency identification
Information and Computation
Classes with easily learnable subclasses
Information and Computation
| Hi-index | 0.00 |
In this paper, we investigate reflective inductive inference of recursive functions. A reflective IIM is a learning machine that is additionally able to assess its own competence.First, we formalize reflective learning from arbitrary example sequences. Here, we arrive at four different types of reflection: reflection in the limit, optimistic, pessimistic and exact reflection.Then, for learning in the limit, for consistent learning of three different types and for finite learning, we compare the learning power of reflective IIMs with each other as well as with the one of standard IIMs.Finally, we compare reflective learning from arbitrary input sequences with reflective learning from canonical input sequences. In this context, an open question regarding total-consistent identification could be solved: it holds T-CONSa 驴 T-CONS.