stem/AI/Classification/Classification.md

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)

\hat i=\text{argmax}_i\left(p(o_t|N_i)\cdot P(N_i)\right)

\hat i=\text{argmax}_i\left(p(o_t|N_i)\right)

• 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

\hat i=\text{argmax}_i\left(P(o_t=k|H_i)\right)

• Nothing else known about classes

\hat i=\text{argmax}_i\left(P(o_t=k|H_i)\cdot P(H_i)\right)

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