Concept Learning

• Assume a set X of possible examples
• A concept is a subset C of X containing all examples for which a given boolean statement is true
• A supervised learning algorithm seeks to learn a function f that correctly classifies any example drawn from X:
  • f(x) = 1 if x is in C
  • f(x) = 0 if x is not in C
• So what is f?
  • Decision tree
  • Anything else that can take an input and return a value
• Multi-label classifiers
  • Can be thought of as learning one concept per label
  • Decision Trees:
    • One tree per label X
    • For each tree, there are two labels: X and not-X