22. A method for updating a classifier, comprising: receiving a sample; assigning a first importance weight to the sample based on a count of samples used to update the classifier; for each of a first plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining, by a hardware processor, an updated error rate of the weak learner based on the outcome of the classification and the first importance weight; selecting a first weak learner from the first plurality based on the updated error rate of the first weak learner; and updating the classifier based on the first weak learner.
22. A method for updating a classifier, comprising: receiving a sample; assigning a first importance weight to the sample based on a count of samples used to update the classifier; for each of a first plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining, by a hardware processor, an updated error rate of the weak learner based on the outcome of the classification and the first importance weight; selecting a first weak learner from the first plurality based on the updated error rate of the first weak learner; and updating the classifier based on the first weak learner. 26. The method of claim 22 , wherein the first importance weight is determined based on a ratio between a count of positive samples used to update the classifier and a count of all samples used to update the classifier.