Patent ID: 7113896
Filing Date: 2006-09-26
Classification: G06Q,G16B

Abstract:
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 +1 and a second class with class label −1, from the data set; b. prescribing an empirical risk minimization problem associated with a classifier model using the selected samples, wherein the empirical risk minimization problem has a plurality of constraints and an objective function which consists of an empirical risk function related to the classification errors of the selected samples by the associated classifier model and an additional term related to the complexity of the classifier model, and the solution of the empirical risk minimization problem adequately defines a classifier to separate the selected samples into the first class and the second class; c. modifying the empirical risk minimization model to include sample-specific importance scores that individually limit the influence of each sample in the solution of the empirical risk minimization problem; and d. solving the modified empirical risk minimization problem to obtain the corresponding classifier to separate the samples into the first class and the second class.