To obtain classification systems with both good generalization performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers 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 majority 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.

To obtain classification systems with both good generalization performance andefficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers arelinear 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 majority 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.

E represents the expectation on random number K of hidden units (1:::; I\:::; n). This relationship makes it possible to characterize explicitly how a regularization term affects bias/variance of networks.

Erepresents the expectation on random number K of hidden units (1 :::; I\ :::; n). This relationship makes it possible to characterize explicitly how a regularization term affects bias/variance of networks.

The former characterizes their "Present Address: Department of Electrical Computer and System Engineering, Rensselaer Poly tech Institute, Troy, NY 12180.

The former characterizes their "Present Address: Department of Electrical Computer and System Engineering, Rensselaer Polytech Institute, Troy, NY 12180.

The former characterizes their "Present Address: Department of Electrical Computer and System Engineering, Rensselaer Poly tech Institute, Troy, NY 12180.