What is the difference between concept learning and classification? I've recently read about concept learning in a machine learning class. They defined a concept as (translated from German):


  Concept:
  
  
  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?
The objective is rather different.
For classification problems, we want to know: GIVEN X, WHAT WILL BE Y?
For concept-learning problems, we are asking: WHAT KIND OF X WILL GIVE US Y?

It may seem similar, but for classification problems, we may use a model that is unable to generalize and answer "WHAT KIND OF X WILL GIVE US Y" until you plug X into the model (e.g. K-nearest neighbor).

On the other hand, concept-learning problems, the main issue is to generalize and answer what can be X specifically.