1. A method in a computer system for generating a classifier based on training samples factoring in inter-sample relationships, the method comprising: providing training samples having inter-sample relationships, a training sample having a feature vector, an initial weight, and an actual classification; and for a plurality of iterations, training a sub-classifier based on the feature vectors, the weights, and the actual classifications of the training samples; classifying the training samples using the trained sub-classifier; adjusting the classifications of the training samples based on the inter-sample relationships; calculating weights for the training samples based on a difference between the classification as adjusted and the actual classification of the training samples; and calculating a weight for the trained sub-classifier; wherein the generated classifier is a combination of the trained sub-classifiers based on the calculated weights of the trained sub-classifiers.