1. A system for updating a classifier, comprising: a hardware processor that is configured to: receive a sample; for each of a first plurality of weak learners, classify the sample using the weak learner, determine an outcome of the classification, and determine an updated error rate of the weak learner based on the outcome of the classification and at least one of: (i) a count of positive samples used to update the classifier, and (ii) a count of negative samples used to update the classifier; select a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and update the classifier based on the first weak learner.
5. The system if claim 1 , wherein the classifier is a linear classifier having the form: 
 F ( x )=sign{Σα× h ( x )}, where h(x) is an output returned by a weak learner h for a data sample x and α is a voting weight.