Bayesian Classification

227 views

Published on

This slides introduced the basic concept and implementation of Bayesian Classification

Published in: Data & Analytics
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
227
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
22
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • http://codepen.io/gangtao/pen/waJvQG
  • http://codepen.io/gangtao/pen/zvjzdb
  • Bayesian Classification

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

    ×