Approaches to classification

• Density estimation coupled with a decision rule
• Define a metric space and decide based on proximity (eg. K nearest neighbour)
• Project the attribute space into decision regions, eg.
  • decision trees: piecewise constant approximation Tree shaped structures that represent sets of decisions
  • perceptrons: linear separators
  • neural nets: non-linear separators These are non-linear predictive models that learn through training