bayes classifier method

Probabilistic Classification Principle
• Maximum A Posterior (MAP) classification rule
– For an input x, find the largest one from L probabilities output by
a discriminative probabilistic classifier
– Assign x to label c* if is the largest.
• Generative classification with the MAP rule
– Apply Bayesian rule to convert them into posterior probabilities
Maximum A Posterior (MAP) classification rule
– For an input x, find the largest one from L probabilities output by
a discriminative probabilistic classifier
– Assign x to label c* if is the largest.
• Generative classification with the MAP rule
– Apply Bayesian rule to convert them into posterior probabilities
– Then apply the MAP rule to assign a label
( ),

Algorithm: Continuous-valued Features
– Numberless values taken by a continuous-valued feature
– Conditional probability often modeled with the normal distribution
If no example contains the feature value
– In this circumstance, we face a zero conditional probability
problem during test
– For a remedy, class conditional probabilities re-estimated with