1. A computer-implemented process for using feature selection to obtain a strong classifier from a combination of weak classifiers, comprising using a computer to perform the following process actions: (a) inputting a set of training examples, a prescribed maximum number of weak classifiers, a cost function capable of measuring the overall cost, and an acceptable maximum cost; (b) computing a set of weak classifiers, each classifier being associated to a particular feature of the training examples, (c ) determining which of the set of weak classifiers is the most significant classifier; (d) adding said most significant classifier to a current set of optimal weak classifiers; (e) determining which of the current set of optimal weak classifiers is the least significant classifier; (f) computing the overall cost for the current set of optimal weak classifiers using the cost function; (g) conditionally removing the least significant classifier for the current set of optimal weak classifiers; (h) computing the overall cost for the current set of optimal weak classifiers less the least significant classifier using the cost function; (i) determining whether the removal of the least significant classifier results in a lower overall cost; (j) whenever it is determined that the removal of the least significant classifier results in a lower overall cost, eliminating the least significant classifier; (k) recomputing each classifier in the current set of optimal weak classifiers associated with a feature added subsequent to the eliminated classifier while keeping the earlier optimal weak classifiers unchanged; (l) repeat actions (f) through (k) until it is determined the removal of the least significant classifier does not result in a lower overall cost and then reinstating the last identified least significant classifier to the current set of optimal weak classifiers; (m) determining if the number of weak classifiers in the current set of optimal weak classifiers equals the prescribed maximum number of weak classifiers or the last computed overall cost for the current set of optimal weak classifiers is less than the acceptable maximum cost; and (n) whenever it is determined that the number of weak classifiers in the current set of optimal weak classifiers does not equal the prescribed maximum number of weak classifiers and the last computed overall cost for the current set of optimal weak classifiers exceeds the acceptable maximum cost, repeating actions (c) through (m) until it is determined that the number of weak classifiers in the current set of optimal weak classifiers does equal the prescribed maximum number of weak classifiers or the last computed overall cost for the current set of optimal weak classifiers becomes less than the maximum allowable cost, then outputting the sum of the individual weak classifiers as the trained strong classifier.