Bayesian Classification

172575eb460a9e859cc6c9ef89b8bb6f?s=47 Gang Tao
November 03, 2015

Bayesian Classification

This slides introduced the basic concept and implementation of Bayesian Classification

172575eb460a9e859cc6c9ef89b8bb6f?s=128

Gang Tao

November 03, 2015
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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

  4. None
  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