Learning from Informants: Relations between Learning Success Criteria

31 Jan 2018 Martin Aschenbach Timo Kötzing Karen Seidel

Learning from positive and negative information, so-called \emph{informants}, being one of the models for human and machine learning introduced by Gold, is investigated. Particularly, naturally arising questions about this learning setting, originating in results on learning from solely positive information, are answered... (read more)

PDF Abstract
No code implementations yet. Submit your code now

Tasks


Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet