1. A computerized method for semi-supervised learning, comprising: receiving a set of training elements; labeling elements of the set of training elements that are determined to fall within a classification group, the set of training elements thereby having labeled elements and unlabeled elements; using selected labeled elements and unlabeled elements as examples in a semi-supervised support vector machine to select a linear classifier; receiving unclassified data elements; and using the selected linear classifier for classifying the received unclassified data elements.