Abstract: A system, method, data processing apparatus, and article of manufacture are provided for classifying data. Labeled data points are received, each of the 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; receiving at least one predetermined cost factor of the labeled data points and unlabeled data points; training a transductive classifier using MED through iterative calculation using the at least one cost factor and the labeled data points and the unlabeled data points as training examples; 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.

Abstract: An improved method of classifying examples into multiple categories using a binary support vector machine (SVM) algorithm.

Abstract: An improved method of classifying examples into multiple categories using a binary support vector machine (SVM) algorithm.

Abstract: An improved method of classifying examples into multiple categories using a binary support vector machine (SVM) algorithm.

Abstract: A system, method, data processing apparatus, and article of manufacture are provided for classifying data. Labeled data points are received, each of the 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; receiving at least one predetermined cost factor of the labeled data points and unlabeled data points; training a transductive classifier using MED through iterative calculation using the at least one cost factor and the labeled data points and the unlabeled data points as training examples; 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.

Abstract: An improved method of classifying examples into multiple categories using a binary support vector machine (SVM) algorithm.

Abstract: An automated method of creating or updating a database, the method comprising,

Abstract: The invention provides a method and system for extracting information from text documents. A document intake module receives and stores a plurality of text documents for processing, an input format conversion module converts each document into a standard format for processing, an extraction module identifies and extracts desired information from each text document, and an output format conversion module converts the information extracted from each document into a standard output format. These modules operate simultaneously on multiple documents in a pipeline fashion so as to maximize the speed and efficiency of extracting information from the plurality of documents.