To balance the diversity and the accuracy in ensemble learning and improve the generalization performance of learning system, a selective ensemble algorithm based on AdaBoost and matching pursuit is proposed. In the algorithm, matching pursuit is fused into the training of AdaBoost, in which the residual between the target function and the linear combination of basis classifiers is minimized with a greedy iterative idea. Then the weight of each basis classifier is updated by the residual which is generated during the last iteration and then the optimal weights for every classifier are gained, by which the component classifiers