Patent ID: 7761391

Claim:
In a computer-based system, a method for classification of data comprising: receiving labeled data points, each of said labeled data points having at least one label indicating whether the data point is a training example for data points for being included in a designated category or a training example for data points being excluded from a designated category; receiving unlabeled data points; training a transductive classifier using Maximum Entropy Discrimination (MED) through iterative calculation using said at least one cost factor and the labeled data points and the unlabeled data points as training examples, wherein for each iteration of the calculations the unlabeled data point cost factor is adjusted as a function of an absolute value of an expected label value of a data point according to: | y |c, where | y | is the absolute value of an expected label value of a data point, and c is the cost factor prior to adjustment thereof, and wherein a data point label prior probability is adjusted according to an estimate of a data point class membership probability; receiving at least one predetermined cost factor of the labeled data points and unlabeled data points; applying the trained classifier to classify at least one of the unlabeled data points, the labeled data points, and input data points; and outputting a classification of the classified data points, or derivative thereof, to at least one of a user, another system, and another process.