Material covered

  • Classification based on class-conditional density
  • Bayes-optimal classification, Bayes risk, discriminants
  • Multivariate Gaussian (normal) distribution review, covariance
  • Discriminants between Gaussian class-conditional densities
  • Linear discriminant in the special case of equal covariance for all classes
  • Comments on the special case of spherical Gaussian densities
  • Discriminants are quadratic surfaces in case of diverse covariances
  • Loss functions: 0/1 ("true"), perceptron, hinge, square
  • Adding loss functions over instances in the model space
  • The perceptron algorithm (proof of convergence deferred)
  • Local regression using kernels
  • Limiting case of impulse kernels supports large-margin principle

Last modified: Saturday, 25 August 2007, 10:14 AM