Explaining the AdaBoost Algorithms to non-technical people

Question

I\'ve been trying to understand the AdaBoost algorithm without much success. I\'m struggling with understanding the Viola Jones paper on Face Detection as an example.

Ca

Answer 1

Adaboost is an algorithm that combines classifiers with poor performance, aka weak learners, into a bigger classifier with much higher performance.

How does it work? In a very simplified manner:

1. Train a weak learner.
2. Add it to the set of weak learners trained so far (with an optimal weight)
3. Increase the importance of samples that are still miss-classified.
4. Go to 1.

There is a broad and detailed theory behind the scenes, but the intuition is just that: let each "dumb" classifier focus on the mistakes the previous ones were not able to fix.

AdaBoost is one of the most used algorithms in the machine learning community. In particular, it is useful when you know how to create simple classifiers (possibly many different ones, using different features), and you want to combine them in an optimal way.

In Viola and Jones, each different type of weak-learner is associated to one of the 4 or 5 different Haar features you can have.