1. A computer-implemented method for generating a classifier from examples in a dataset containing malicious noise, comprising: storing in a memory at least one of the examples of the dataset, wherein the at least one examples stored in the memory contains malicious noise; generating with at least one processor a plurality of candidate classifiers, wherein at least some of the plurality of candidate classifiers comprise a majority vote over a plurality of randomly-generated classifiers as applied to the at least one example stored in the memory, wherein the plurality of randomly-generated classifiers has a cardinality that is a function of a size of a margin of the randomly-generated classifiers; and generating a boosted classifier that incorporates at least some of the plurality of candidate classifiers.