Co-learning of Functions by Probabilistic Algorithms
Kucevalovs Ilja, Balodis Kaspars, Freivalds Rusinš
Available Online April 2013.
- 10.2991/3ca-13.2013.18How to use a DOI?
- inductive inference; co-learning; probabilistic algorithms
We investigate properties of an identification type of recursive functions, called co-learning. The inductive process refutes all possible programs but one, and, by definition, this program is demanded to be correct. This type of identification was introduced in . M. Kummer in the paper  showed that this type characterizes computable numberings possessing a certain property thus answering a long standing open problem by Yu. L. Ershov . We consider probabilistic algorithms of co-learning and establish an infinite discrete hierarchy of classes of recursive functions. The parameters of this new hierarchy coincide with the hierarchy by R. Freivalds  for probabilistic algorithms of finite identification.
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kucevalovs Ilja AU - Balodis Kaspars AU - Freivalds Rusinš PY - 2013/04 DA - 2013/04 TI - Co-learning of Functions by Probabilistic Algorithms BT - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation PB - Atlantis Press SP - 71 EP - 73 SN - 1951-6851 UR - https://doi.org/10.2991/3ca-13.2013.18 DO - 10.2991/3ca-13.2013.18 ID - Ilja2013/04 ER -