1. A method for processing information in a data set that contains samples of at least two classes using an empirical risk minimization model, wherein each sample in the data set has an importance score, comprising: a. selecting samples of a first class being labeled with class label&plus;1 and a second class with class label−1, from the data set; b. prescribing an empirical risk minimization model using the selected samples with an objective function and a plurality of constraints which adequately describes the solution of a classifier to separate the selected samples into the first class and the second class; c. modifying the empirical risk minimization model to include terms that individually limit the influence of each sample relative to its importance score in the solution of the empirical risk minimization model; and d. solving the modified empirical risk minimization model to obtain the corresponding classifier to separate the samples into the first class and the second class.