Understanding Classification Approaches

Discriminative vs Generative Classifiers

1. Discriminative Classifiers (e.g., Linear Perceptrons)

   • Learn decision boundaries directly
   • Effective for well-separated classes
   • Focus on class separation
   • Simpler to implement

2. Generative Classifiers (Bayesian Approach)

   • Model each class as a random vector
   • Use probability distributions/density functions
   • Consider class likelihoods
   • More flexible in handling complex data

Bayesian Classification

The experiment focuses on understanding how Bayesian classification:

• Computes class probabilities using Bayes' rule
• Combines prior knowledge with observed evidence
• Makes decisions based on posterior probabilities
• Handles uncertainty in classification

Key Concepts

• Prior probabilities
• Likelihood functions
• Posterior probabilities
• Decision boundaries
• Class density functions