Accelerating the boosting approach to training classifiers

摘要

Systems, methods, and computer program products implementing techniques for training classifiers. The techniques include receiving a training set that includes positive samples and negative samples, receiving a restricted set of linear operators, and using a boosting process to train a classifier to discriminate between the positive and negative samples. The boosting process is an iterative process. The iterations include a first iteration where a classifier is trained by (1) testing some, but not all linear operators in the restricted set against a weighted version of the training set, (2) selecting for use by the classifier the linear operator with the lowest error rate, and (3) generating a re-weighted version of the training set. The iterations also include subsequent iterations during which another classifier is trained by repeating steps (1), (2), and (3), but using in step (1) the re-weighted version of the training set generated during a previous iteration.