1. A computer-readable medium having computer executable instructions stored thereon for automatically learning a set of weak classifiers of a combination classifier, said computer executable instructions comprising: receiving a set of positive examples and a set of negative examples of instances in one or more sets of training data; randomly sampling the set of negative examples to form a subset of negative examples; initializing a weight for each example of the set of positive examples and the subset of negative examples; for each of a predetermined number of weak classifiers, iteratively applying adaptive boosting-based learning to the set of positive examples and the subset of negative examples for a number of iterations to automatically learn a set of weak classifiers; iteratively weight trimming examples from the set of negative examples and reforming the subset of negative examples while iteratively applying adaptive boosting-based learning; and outputting the learned set of weak classifiers.
1. A computer-readable medium having computer executable instructions stored thereon for automatically learning a set of weak classifiers of a combination classifier, said computer executable instructions comprising: receiving a set of positive examples and a set of negative examples of instances in one or more sets of training data; randomly sampling the set of negative examples to form a subset of negative examples; initializing a weight for each example of the set of positive examples and the subset of negative examples; for each of a predetermined number of weak classifiers, iteratively applying adaptive boosting-based learning to the set of positive examples and the subset of negative examples for a number of iterations to automatically learn a set of weak classifiers; iteratively weight trimming examples from the set of negative examples and reforming the subset of negative examples while iteratively applying adaptive boosting-based learning; and outputting the learned set of weak classifiers. 7. The computer-readable medium of claim 1 wherein iteratively weight trimming examples comprises removing a desired percentage of the lowest weighted negative examples from the set of negative examples.