1. A process for constructing a set of weak classifiers for use in generating a combination classifier, comprising: receiving a set of weighted positive and weighted negative examples; wherein each positive example represents a portion of training data corresponding to one of a plurality of targets represented in the training data, and wherein each negative example represents a portion of the training data not corresponding to any of the targets; selecting a filter for each of a predetermined number of weak classifiers; for each filter, constructing a binned histogram of the weights of the positive and negative examples by iteratively splitting bin sizes of each histogram into a number of flexible, variably sized bins; and outputting each histogram to form a set of weak classifiers.