Abstract: System, method and computer program product provides a novel domain adaption/transfer learning approach applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The proposed method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain. Exemplary results provided for a Twitter dataset demonstrate that the method identifies meaningful hidden topics and provides useful classifications of specific tweets.

Abstract: System, method and computer program product provides a novel domain adaption/transfer learning approach applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The proposed method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain. Exemplary results provided for a Twitter dataset demonstrate that the method identifies meaningful hidden topics and provides useful classifications of specific tweets.

Abstract: A method for identifying companies with specific business objectives that includes using existing sources of company firmographic data to identify a broad set of companies and associated websites, crawling the websites associated with the identified companies and indexing web site content for each of the identified companies with the specific business objective to realize indexed web content. The method further includes joining the company firmographic data with the indexed web content using a business objective common identifier to generate a store of joined structured firmographic data and indexed web content and presenting a display image representation of the store of joined structured firmographic data and indexed web content for user review. The display image further receives user input to score each of said companies identified therein, and using a search interface, querying the store of scored, joined structured firmographic data and indexed web content.

Abstract: Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. A graph-based transfer learning framework propagates label information from a source domain to a target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bipartite graph. An iterative algorithm renders the framework scalable to large-scale applications. The framework propagates the label information to both features irrelevant to the source domain and unlabeled examples in the target domain via common features in a principled way.

Abstract: A model for impact analysis determines impact of part removal from a product. An entity is identifies that includes a plurality of sub-components. One or more performance measures associated with the entity are identified. One or more of the sub-components to be removed from the entity are identified. A substitution impact function is defined. Impact on said one or more performance measures is determined using the substitution impact function.

Abstract: A method for automatically determining an Internet home page corresponding to a named entity identified by a specified descriptor including building a trained machine-learning model, generating candidate matches from the specified descriptor, wherein each candidate match includes an Internet address, extracting content-based features from websites associated with the Internet addresses of the candidate matches, determining a model score for each candidate match based on the content-based features using the trained machine-learning model, and determining a match from among the candidate matches according to the scores, wherein the match is returned as the Internet home page corresponding to the named entity.

Abstract: A method for identifying companies with specific business objectives that includes using existing sources of company firmographic data to identify a broad set of companies and associated websites, crawling the websites associated with the identified companies and indexing web site content for each of the identified companies with the specific business objective to realize indexed web content. The method further includes joining the company firmographic data with the indexed web content using a business objective common identifier to generate a store of joined structured firmographic data and indexed web content and presenting a display image representation of the store of joined structured firmographic data and indexed web content for user review. The display image further receives user input to score each of said companies identified therein, and using a search interface, querying the store of scored, joined structured firmographic data and indexed web content.

Abstract: The present invention employs data processing systems to handle debt collection by formulation the collections process as a Markov Decision Process with constrained resources, thus making it possible automatically to generate an optimal collections policy with respect to maximizing long-term expected return throughout the course of a collections process, subject to constraints on the available resources possibly in multiple organizations. This is accomplished by coupling data modeling and resource optimization within the constrained Markov Decision Process formulation and generating optimized rules based on constrained reinforcement learning process comprising applied on the basis of past historical data.

Abstract: The present invention employs data processing systems to handle debt collection by formulation the collections process as a Markov Decision Process with constrained resources, thus making it possible automatically to generate an optimal collections policy with respect to maximizing long-term expected return throughout the course of a collections process, subject to constraints on the available resources possibly in multiple organizations. This is accomplished by coupling data modeling and resource optimization within the constrained Markov Decision Process formulation and generating optimized rules based on constrained reinforcement learning process comprising applied on the basis of past historical data.

Abstract: A method (and structure) for developing a distribution function for the probability of winning a bid by a seller for a product or service, using the seller's own historical data for winning bids and lost bids, includes normalizing the data for winning bids and the data for lost bids and merging the normalized data into a single set of data.

Abstract: A method (and structure) for developing a distribution function for the probability of winning a bid by a seller for a product or service, using the seller's own historical data for winning bids and lost bids, includes normalizing the data for winning bids and the data for lost bids and merging the normalized data into a single set of data.