Classification

Given an observation, determine one class from a set of classes that best explains the observation

Features are discrete or continuous

• 2 category classifier
  • Dichotomiser

Argmax

Argument that gives the maximum value from a target function

Gaussian Classifier

Training

• Each class $i$ has it’s own Gaussian $N_i=N(m_i,v_i)$

• With equal priors

Discrete Classifier

• Each class $i$ has it’s own histogram $H_i$
  • Describes the probability of each observation type $k$
  • $P(o_t=k|H_i)$, based on class-specific type counts

• Nothing else known about classes

• Given class priors $P(H_i)$
• Maximum posterior probability
  • Bayes