16. A non-transitory computer-readable medium storing a program of instructions executable by one or more machines to perform a method for generating a classifier from examples in a dataset containing malicious noise, the method comprising: storing in a computer memory at least one of the examples of the dataset, wherein the at least one examples stored in the memory contains malicious noise; repeatedly calling a weak learner module that (i) determines a plurality of randomly-generated classifiers and a plurality of candidate classifiers that comprise a majority vote over at least some of the plurality of randomly-generated classifiers, 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 (ii) determines a plurality of weak classifiers that are selected from the plurality of candidate classifiers based on the error rate realized using the selected ones of the plurality of candidate classifiers to classify examples drawn from the dataset; and generating a target classifier that incorporates at least some of the plurality of weak classifiers.
20. The non-transitory computer-readable medium of claim 16 , wherein the weak learner module generates the plurality of randomly-generated classifiers using a plurality of random vectors whose elements correspond to a plurality of features found in the examples.