Patent application number | Description | Published |
20080275800 | METHOD AND SYSTEM FOR DEBT COLLECTION OPTIMIZATION - 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. | 11-06-2008 |
20090204569 | METHOD AND SYSTEM FOR IDENTIFYING COMPANIES WITH SPECIFIC BUSINESS OBJECTIVES - 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. The method further includes augmenting the search interface, or search results from a query, with predictive, machine-leaning processes that allow rapid identification of companies possibly missed in the query. | 08-13-2009 |
20090210419 | METHOD AND SYSTEM USING MACHINE LEARNING TO AUTOMATICALLY DISCOVER HOME PAGES ON THE INTERNET - 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. | 08-20-2009 |
20110251877 | MODEL FOR MARKET IMPACT ANALYSIS OF PART REMOVAL FROM COMPLEX PRODUCTS - 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. | 10-13-2011 |
20110320387 | Graph-based transfer learning - 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. | 12-29-2011 |
20120185415 | SYSTEM AND METHOD FOR DOMAIN ADAPTION WITH PARTIAL OBSERVATION - 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. | 07-19-2012 |
20130013539 | SYSTEM AND METHOD FOR DOMAIN ADAPTION WITH PARTIAL OBSERVATION - 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. | 01-10-2013 |
20130013540 | GRAPH-BASED TRANSFER LEARNING - 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. | 01-10-2013 |
20130018827 | SYSTEM AND METHOD FOR AUTOMATED LABELING OF TEXT DOCUMENTS USING ONTOLOGIESAANM He; JingruiAACI OssiningAAST NYAACO USAAGP He; Jingrui Ossining NY USAANM Lawrence; Richard D.AACI RidgefieldAAST CTAACO USAAGP Lawrence; Richard D. Ridgefield CT USAANM Melville; PremAACI White PlainsAAST NYAACO USAAGP Melville; Prem White Plains NY USAANM Sindhwani; VikasAACI HawthorneAAST NYAACO USAAGP Sindhwani; Vikas Hawthorne NY USAANM Chenthamarakshan; Vijil E.AACI OssiningAAST NYAACO USAAGP Chenthamarakshan; Vijil E. Ossining NY US - A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution. | 01-17-2013 |
20130018828 | SYSTEM AND METHOD FOR AUTOMATED LABELING OF TEXT DOCUMENTS USING ONTOLOGIES - A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution. | 01-17-2013 |
20130041860 | PREDICTING INFLUENCE IN SOCIAL NETWORKS - A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users. | 02-14-2013 |
20130151520 | INFERRING EMERGING AND EVOLVING TOPICS IN STREAMING TEXT - A method, system and computer program product for inferring topic evolution and emergence in a set of documents. In one embodiment, the method comprises forming a group of matrices using text in the documents, and analyzing these matrices to identify a first group of topics as evolving topics and a second group of topics as emerging topics. The matrices includes a first matrix X identifying a multitude of words in each of the documents, a second matrix W identifying a multitude of topics in each of the documents, and a third matrix H identifying a multitude of words for each of the multitude of topics. These matrices are analyzed to identify the evolving and emerging topics. In an embodiment, the documents form a streaming dataset, and two forms of temporal regularizers are used to help identify the evolving topics and the emerging topics in the streaming dataset. | 06-13-2013 |
20130151525 | INFERRING EMERGING AND EVOLVING TOPICS IN STREAMING TEXT - A method, system and computer program product for inferring topic evolution and emergence in a set of documents. In one embodiment, the method comprises forming a group of matrices using text in the documents, and analyzing these matrices to identify evolving topics and emerging topics. The matrices includes a matrix X identifying a multitude of words in each of the documents, a matrix W identifying a multitude of topics in each of the documents, and a matrix H identifying a multitude of words for each of the multitude of topics. These matrices are analyzed to identify the evolving and emerging topics. In an embodiment, two forms of temporal regularizers are used to help identify the evolving and emerging topics. In another embodiment, a two stage approach involving detection and clustering is used to help identify the evolving and emerging topics. | 06-13-2013 |
20130325756 | GRAPH-BASED FRAMEWORK FOR MULTI-TASK MULTI-VIEW LEARNING - A system and method a Multi-Task Multi-View (M | 12-05-2013 |