The Gentle Ada Boost Algorithm  is used to select a set of simple CARTs to achieve a given detection and error rate . In the following, a detection is referred to as a hit and an error as a false alarm.
The learning is based on weighted training examples , where are the images and the classified output. At the beginning of the learning phase the weights are initialized with . The following three steps are repeated to select CARTs until a given detection rate is reached:
The final output of the classifier is sign, with the weighted return value of the CART. Next, a cascade based on these classifiers is built.