1. A method performed by a computer system for classifying data with a classification system when training data that is used to train the classification system potentially contains errors, comprising: dividing by the computer system a full set of the training data into a positive subset and a negative subset, the positive subset containing all training data with a positive classification value and the negative subset containing all training data with a negative classification value; creating at least two randomly constructed subsets of the full set of training data wherein there is equal probability any element selected for one of the two randomly constructed subsets will be from the positive and negative subsets further wherein a count of data in each randomly created subset is less than one percent of a count of the full set of training data; and creating at least two classifiers, wherein each created classifier is a support vector machine for which multiple expressions in the support vector machine are based on one of the randomly constructed subsets, and each randomly constructed subset is used to create only one classifier.