bayesian classification
TRANSCRIPT
Bayesian ClassifierGang Tao
Algebraic GeometryComplex Analysis factal Differential equationGeometry
Dynamical SystemCombinatorial Mathematics
StatisticsComputational mathematics
Bayes Theorem
Bayes Theorem
Diachronic Interpretation
H -> HypothesisD -> DataP(H) -> Prior ProbabilityP(H|D) -> Posterior ProbabilityP(D|H) -> LikelihoodP(D) -> Normalizing Constant
Bayes Theorem
Original Belief Observation+ = New Belief
Bayes and Occam’s Razor
“All Models are wrong, but some of them are better
than the others”
Model Complexity
Naive Bayes
“Naive” because it is based on independence assumptionAll the attributes are conditional independent given the class
Naive Bayes Classifier
How to build a Bayesian Classifier for prediction
Prepare Data Features Extraction
Select Distribution
Model
Calculate the Probability for each attributes
Multiply All Probabilities
Label with highest
Probability
Advantage VS. Disadvantage
PowerfulEfficient in Space and TimeIncremental Trainer
SimpleIndependant AssumptionProbability are not relevant
Application of Bayesian Classifier
Spam Email Filter
Natural Language Processing
Word Segmentation
Spell Checking
Machine Translation
Pattern Recognition
Thank You