Boosting

In the context of supervised learning we define weak learners as learners that are just a little better than a baseline such as randomly assigning classes or average values. Although weak learners are weak individually like ants, together they can do amazing things just like ants can. It makes sense to take into account the strength of each individual learner using weights. This general idea is called boosting. There are many boosting algorithms; boosting algorithms differ mostly in their weighting scheme. If you have studied for an exam, you may have applied a similar technique by identifying the type of practice questions you had trouble with and focusing on the hard problems.

Face detection in images is based on a specialized framework, which also uses boosting. Detecting faces in images or videos is a supervised learning. We give the learner examples of regions containing faces. There is an imbalance, since we usually have far more regions (about ten thousand times more) that don't have faces. A cascade of classifiers progressively filters out negative image areas stage by stage. In each progressive stage, the classifiers use progressively more features on fewer image windows. The idea is to spend the most time on image patches, which contain faces. In this context, boosting is used to select features and combine results.