| AdaBoost:
Intuition |
|
|
|
|
|
|
|
|
|
|
| Consider a 2-d feature |
|||||||||
| space with positive and |
|||||||||
| negative
examples. |
|||||||||
| Each weak classifier splits |
|||||||||
| the training examples with |
|||||||||
| at least 50% accuracy. |
|||||||||
| Examples misclassified by |
|||||||||
| a previous weak learner |
|||||||||
| are given more emphasis |
|||||||||
| at
future rounds. |
|||||||||
| Figure
adapted from Freund and Schapire |
| 53 |
| K. Grauman, B. Leibe |