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AdaBoost

AdaBoost. Classifier. Simplest classifier. Adaboost : Agenda. ( Ada ptive Boost ing, R. Scharpire , Y. Freund, ICML, 1996): Supervised classifier Assembling classifiers Combine many low-accuracy classifiers (weak learners) to create a high-accuracy classifier (strong learners ).

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AdaBoost

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  1. AdaBoost

  2. Classifier • Simplest classifier

  3. Adaboost: Agenda • (Adaptive Boosting, R. Scharpire, Y. Freund, ICML, 1996): • Supervised classifier • Assembling classifiers • Combine many low-accuracy classifiers (weak learners) to create a high-accuracy classifier (strong learners)

  4. Example 1

  5. Adaboost: Example (1/10)

  6. Adaboost: Example (2/10)

  7. Adaboost: Example (3/10)

  8. Adaboost: Example (4/10)

  9. Adaboost: Example (5/10)

  10. Adaboost: Example (6/10)

  11. Adaboost: Example (7/10)

  12. Adaboost: Example (8/10)

  13. Adaboost: Example (9/10)

  14. Adaboost: Example (10/10)

  15. Adaboost • Strong classifier = linear combination of T weak classifiers (1) Design of weak classifier (2) Weight for each classifier (Hypothesis weight) (3) Update weight for each data (example distribution) • Weak Classifier: < 50% error over any distribution

  16. Adaboost: Terminology (1/2)

  17. Adaboost: Terminology (2/2)

  18. Adaboost: Framework

  19. Adaboost: Framework

  20. Adaboost • Strong classifier = linear combination of T weak classifiers (1) Design of weak classifier (2) Weight for each classifier (Hypothesis weight) (3) Update weight for each data (example distribution) • Weak Classifier: < 50% error over any distribution

  21. Adaboost: Design of weak classifier (1/2)

  22. Adaboost: Design of weak classifier (2/2) • Select a weak classifier with thesmallest weighted error • Prerequisite:

  23. Adaboost • Strong classifier = linear combination of T weak classifiers (1) Design of weak classifier (2) Weight for each classifier (Hypothesis weight) (3) Update weight for each data (example distribution) • Weak Classifier: < 50% error over any distribution

  24. Adaboost: Hypothesis weight (1/2) • How to set ?

  25. Adaboost: Hypothesis weight (2/2)

  26. Adaboost • Strong classifier = linear combination of T weak classifiers (1) Design of weak classifier (2) Weight for each classifier (Hypothesis weight) (3) Update weight for each data (example distribution) • Weak Classifier: < 50% error over any distribution

  27. Adaboost: Update example distribution (Reweighting) y * h(x) = 1 y * h(x) = -1

  28. Reweighting In this way, AdaBoost “focused on” the informative or “difficult” examples.

  29. Reweighting In this way, AdaBoost “focused on” the informative or “difficult” examples.

  30. Summary t = 1

  31. Example 2

  32. Example (1/5) Original Training set : Equal Weights to all training samples Taken from “A Tutorial on Boosting” by Yoav Freund and Rob Schapire

  33. Example (2/5) ROUND 1

  34. Example (3/5) ROUND 2

  35. Example (4/5) ROUND 3

  36. Example (5/5)

  37. Example 3

  38. Example 4

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