Bayesian Classification

This slides introduced the basic concept and implementation of Bayesian Classification

Transcript

1. Bayesian Classifier Gang Tao
2. Algebraic Geometry Complex Analysis factal Differential equation Geometry Dynamical System Combinatorial Mathematics Statistics Computational mathematics
3. Bayes Theorem
5. Bayes Theorem
6. Diachronic Interpretation H -> Hypothesis D -> Data P(H) -> Prior Probability P(H|D) -> Posterior Probability P(D|H) -> Likelihood P(D) -> Normalizing Constant
7. Bayes Theorem Original Belief Observation + = New Belief
8. Bayes and Occam’s Razor
9. “All Models are wrong, but some of them are better than the others”
10. Model Complexity
11. Naive Bayes “Naive” because it is based on independence assumption All the attributes are conditional independent given the class
12. Naive Bayes Classifier
13. 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
14. Advantage VS. Disadvantage Powerful Efficient in Space and Time Incremental Trainer Simple Independant Assumption Probability are not relevant
15. Application of Bayesian Classifier Spam Email Filter Natural Language Processing Word Segmentation Spell Checking Machine Translation Pattern Recognition
16. Thank You