14. A method for constructing a set of weak classifiers of a combination classifier, comprising using a computing device to: receive a set of positive examples of instances in a set of training data and a set of negative examples of instances in the set of training data; weighting each positive and negative example; selecting a unique filter for each of a predetermined number of weak classifiers; applying each of the unique filters to the sets of weighted positive and negative examples, and constructing a unique histogram of the filtered weighted positive and negative examples for each of the unique filters; transforming each histogram into a histogram having a variable number of variably sized bins; and outputting each transformed histogram as a unique weak classifier to form a set of weak classifiers.
14. A method for constructing a set of weak classifiers of a combination classifier, comprising using a computing device to: receive a set of positive examples of instances in a set of training data and a set of negative examples of instances in the set of training data; weighting each positive and negative example; selecting a unique filter for each of a predetermined number of weak classifiers; applying each of the unique filters to the sets of weighted positive and negative examples, and constructing a unique histogram of the filtered weighted positive and negative examples for each of the unique filters; transforming each histogram into a histogram having a variable number of variably sized bins; and outputting each transformed histogram as a unique weak classifier to form a set of weak classifiers. 17. The method of claim 14 wherein transforming each histogram into a histogram having a variable number of variably sized bins comprises optimizing bin size by maximizing a ratio of positive to negative examples for each bin.