baysein ethod

Probabilistic Classification Principle
• Establishing a probabilistic model for classification – Discriminative model

• To train a discriminative classifier regardless its probabilistic or nonprobabilistic nature, all training examples of different
classes must be jointly used to build up a single discriminative classifier.
• Output L probabilities for L class
labels in a probabilistic classifier while a single label is achieved by a non-probabilistic classifier .

Continuous-valued Features
– Numberless values taken by a continuous-valued feature
– Conditional probability often modeled with the normal distribution

– Learning Phase:
Output: normal distributions

– Test Phase: Given an unknown instance
• Instead of looking-up tables, calculate conditional probabilities with all the normal distributions achieved in the learning phrase
• Apply the MAP rule to assign a label (the same as done for the discrete case)

Zero conditional probability
• 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