1. A method in a computer system with a processor and memory 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 sample weight, and an actual classification; setting a current sample weight for each training sample to the initial sample weight of the training sample; and for a plurality of iterations performed by the processor, selecting for this iteration a sub-classifier that has not yet been trained; training the selected sub-classifier based on the feature vectors, the current sample weights, and the actual classifications of the training samples; classifying the training samples using the trained sub-classifier such that each training sample has a classification for this iteration; adjusting the classifications of the training samples for this iteration based on the inter-sample relationships between the training samples; adjusting current sample weights for the training samples based on a difference between the classifications for this iteration as adjusted and the actual classification of the training samples such that the adjusted current sample weights are used to train the sub-classifier of the next iteration; and calculating a sub-classifier weight for the trained sub-classifier of this iteration based on the classifications of this iteration as adjusted that are different from the actual classifications of the training samples; wherein the generated classifier is a combination of the trained sub-classifiers based on the calculated sub-classifier weights of the trained sub-classifiers.
1. A method in a computer system with a processor and memory 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 sample weight, and an actual classification; setting a current sample weight for each training sample to the initial sample weight of the training sample; and for a plurality of iterations performed by the processor, selecting for this iteration a sub-classifier that has not yet been trained; training the selected sub-classifier based on the feature vectors, the current sample weights, and the actual classifications of the training samples; classifying the training samples using the trained sub-classifier such that each training sample has a classification for this iteration; adjusting the classifications of the training samples for this iteration based on the inter-sample relationships between the training samples; adjusting current sample weights for the training samples based on a difference between the classifications for this iteration as adjusted and the actual classification of the training samples such that the adjusted current sample weights are used to train the sub-classifier of the next iteration; and calculating a sub-classifier weight for the trained sub-classifier of this iteration based on the classifications of this iteration as adjusted that are different from the actual classifications of the training samples; wherein the generated classifier is a combination of the trained sub-classifiers based on the calculated sub-classifier weights of the trained sub-classifiers. 2. The method of claim 1 wherein the generated classifier is an adaptive boosting classifier.