9. A method for automatically learning a set of weak classifiers for constructing a combination classifier, comprising using a computing device to: receive a set of positive examples of instances represented in training data; receive a set of negative examples of the instances; form a subset of negative examples from the set of negative examples; set an initial weight for each positive example; set an initial weight for each negative example of the subset of negative examples; automatically learn a predetermined number of weak classifiers by iteratively applying adaptive boosting-based learning to the set of positive examples and the subset of negative examples for a predetermined number of iterations; iteratively weight trim examples from the set of negative examples and reform the subset of negative examples while iteratively applying the adaptive boosting-based learning; and constructing a combination classifier from the learned weak classifiers.
9. A method for automatically learning a set of weak classifiers for constructing a combination classifier, comprising using a computing device to: receive a set of positive examples of instances represented in training data; receive a set of negative examples of the instances; form a subset of negative examples from the set of negative examples; set an initial weight for each positive example; set an initial weight for each negative example of the subset of negative examples; automatically learn a predetermined number of weak classifiers by iteratively applying adaptive boosting-based learning to the set of positive examples and the subset of negative examples for a predetermined number of iterations; iteratively weight trim examples from the set of negative examples and reform the subset of negative examples while iteratively applying the adaptive boosting-based learning; and constructing a combination classifier from the learned weak classifiers. 13. The method of claim 9 further comprising defining a set of global weight update intervals.