Chuanyi Ji, Sheng Ma
To obtain classification systems with both good generalization per(cid:173) formance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak clas(cid:173) sifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They· are then combined through a ma(cid:173) jority vote. As demonstrated through systematic experiments, the method developed is able to obtain combinations of weak classifiers with good generalization performance and a fast training time on a variety of test problems and real applications.