I've recently read about concept learning in a machine learning class. They defined a concept as (translated from German):
- Describes a subset of objects or events defined on a bigger set.
- Boolean function defined on bigger set
Given: Examples which are labeled as members or not-members
Searched: Automatically infer the definition of the underlying concept
Definition of concept learning: Infering a boolean-valued function from training data of its input and its output.
I would call this a 2-class classification problem. Is there a difference?