A systematic comparison between different base learners in AdaBoosting model

Abstract

Boosting methods are becoming popular due their outstanding performance when compared with others statistical learning techniques. The Adaptive Boosting consists in a linear combination of weak models to built a strong classifier.This work proposes a systematic comparison between the possible models that can be used as base classifiers.