Probabilistic Learning of Indexed Families under Monotonicity Constraints: Hierarchy Results and Complexity Aspects

In our work, we deal with a formalization of inductive inference. We investigate a class of probabilistic inductive inference models, namely probabilistic inductive inference of indexed families under <br>monotonicity constraints. All these inference models are based <br>on the inductive inference model introduced by Gold. We <br>discuss in detail how powerful these probabilistic learning models <br>are in contrast to their deterministic counterparts and introduce <br>a complexity measure in order to measure the additional power of <br>the probabilistic machines in qualitative terms.

Author dc:creator

• Meyer, Lea

Contributors dc:contributor

• Schinzel, Britta

• Induktive Inferenz
• Probabilistisches Lernen
• Formale Sprachen
• Inductive Inference
• Probabilistic Learning
• Complexity Theory

Repository record source_url

https://freidok.uni-freiburg.de/data/203

OAI identifier oai:identifier

oai:freidok.uni-freiburg.de:203