Abstract: Embodiments of the invention relate to improvements to the support vector machine (SVM) classification model. When text data is significantly unbalanced (i.e., positive and negative labeled data are in disproportion), the classification quality of standard SVM deteriorates. Embodiments of the invention are directed to a weighted proximal SVM (WPSVM) model that achieves substantially the same accuracy as the traditional SVM model while requiring significantly less computational time. A weighted proximal SVM (WPSVM) model in accordance with embodiments of the invention may include a weight for each training error and a method for estimating the weights, which automatically solves the unbalanced data problem.

Abstract: An attribute information acquisition method, including transmitting an attribute request for contents attribute information for altering attributes of contents data stored in a storage medium to an attribute information providing apparatus. The method also includes receiving the contents attribute information transmitted from the attribute information providing apparatus as a result of the transmission of the attribute request. Further, the method includes transmitting to an accounting status notification apparatus a notification request for accounting status notification information that indicates whether an accounting process that corresponds to the contents attribute information has been completed, when a communication enabled state with the attribute information providing apparatus is restored after communication with the attribute information providing apparatus is interrupted during the receiving the contents attribute information.

Abstract: Methods are disclosed for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs. In operation, usage job data of observed users and devices carrying out the jobs is recorded. A probability model is defined with an observed user variable, an observed device variable, a latent job cluster variable, and a latent job service class variable. A range of job service classes associated with the shared devices is determined, and an initial number of job clusters is selected. Parameters of the probability model are learned using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters. The learned parameters of the probability model are applied to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.