公开(公告)号：US09031888B2

公开(公告)日：2015-05-12

申请号：US13206981

申请日：2011-08-10

申请人： Richard D. Lawrence ,  Estepan Meliksetian ,  Prem Melville ,  Claudia Perlich ,  Karthik Subbian

发明人： Richard D. Lawrence ,  Estepan Meliksetian ,  Prem Melville ,  Claudia Perlich ,  Karthik Subbian

IPC分类号： G06Q10/10 ,  G06Q50/00

CPC分类号： G06N5/04 ,  G06N99/005 ,  G06Q10/10 ,  G06Q30/0201 ,  G06Q50/01

摘要： 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.

摘要翻译： 公开了用于预测社交网络中的影响的方法，系统和计算机程序产品。 在一个实施例中，该方法包括识别社交网络的一组用户，并且基于定义的标准将用户的子集识别为有影响力的用户。 许多措施被确定为一组用户中有哪些是有影响力的用户的预测因子。 这些度量被聚合，并且基于该聚合形成复合预测器模型。 这种复合预测模型用于预测未来在社交网络中哪一组用户将具有特定的影响。 在一个实施例中，指定的影响基于从用户发送的消息，并且例如可以基于由其他用户重新发送的每个用户发送的消息的数量。

公开(公告)号：US08990128B2

公开(公告)日：2015-03-24

申请号：US13488885

申请日：2012-06-05

申请人： Jingrui He ,  David C. Gondek ,  Richard D. Lawrence ,  Enara C. Vijil

发明人： Jingrui He ,  David C. Gondek ,  Richard D. Lawrence ,  Enara C. Vijil

IPC分类号： G06F15/18 ,  G06K9/62

CPC分类号： G06K9/628

摘要： A system and method a Multi-Task Multi-View (M2TV) learning problem. The method uses the label information from related tasks to make up for the lack of labeled data in a single task. The method further uses the consistency among different views to improve the performance. It is tailored for the above complicated dual heterogeneous problems where multiple related tasks have both shared and task-specific views (features), since it makes full use of the available information.

摘要翻译： 多任务多视图（M2TV）学习问题的系统和方法。 该方法使用相关任务的标签信息来弥补单个任务中缺少标记数据。 该方法进一步使用不同视图之间的一致性来提高性能。 它针对上述复杂的双重异构问题，其中多个相关任务具有共享和任务特定的视图（特征），因为它充分利用了可用的信息。

公开(公告)号：US08856052B2

公开(公告)日：2014-10-07

申请号：US13618603

申请日：2012-09-14

申请人： Vijil E. Chenthamarakshan ,  Richard D. Lawrence ,  Yan Liu ,  Dan Zhang

发明人： Vijil E. Chenthamarakshan ,  Richard D. Lawrence ,  Yan Liu ,  Dan Zhang

IPC分类号： G06F15/18

CPC分类号： G06N99/005 ,  G06F17/3071

摘要： A novel domain adaption/transfer learning method applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The 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.

公开(公告)号：US20130018827A1

公开(公告)日：2013-01-17

申请号：US13184156

申请日：2011-07-15

申请人： Jingrui He ,  Richard D. Lawrence ,  Prem Melville ,  Vikas Sindhwani ,  Vijil E. Chenthamarakshan

发明人： Jingrui He ,  Richard D. Lawrence ,  Prem Melville ,  Vikas Sindhwani ,  Vijil E. Chenthamarakshan

IPC分类号： G06F15/18 ,  G06N5/02

CPC分类号： G06N5/022 ,  G06F16/353 ,  G06F16/367

摘要： 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.

摘要翻译： 第一映射函数自动地将多个文档映射到本体的概念上以创建文档到本体分布。 接收将本体中的概念分别映射到类标签的本体到类的分发，并且生成一个分类器，该分类器将所选择的文档标记为基于文档到本体分布和本体 - 班级分配。

公开(公告)号：US20130013539A1

公开(公告)日：2013-01-10

申请号：US13618603

申请日：2012-09-14

申请人： Vijil E. Chenthamarakshan ,  Richard D. Lawrence ,  Yan Liu ,  Dan Zhang

发明人： Vijil E. Chenthamarakshan ,  Richard D. Lawrence ,  Yan Liu ,  Dan Zhang

IPC分类号： G06F15/18

CPC分类号： G06N99/005 ,  G06F17/3071

摘要： 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.

摘要翻译： 系统，方法和计算机程序产品提供了一种新颖的域适应/转移学习方法，其应用于对简短文档进行分类的问题，例如短文本消息，即时消息，推文。 所提出的方法使用大量多标记示例（源域）来改善部分观察（目标域）上的学习。 具体来说，学习一个隐藏的，更高级别的抽象空间，这对于源域中的多标签示例是有意义的。 这是通过使用来自源域的已知标签在隐藏空间中学习的分类模型中同时最小化文档重建错误和错误来完成的。 然后将目标空间中的部分观察值映射到相同的隐藏空间，并将其分类为由源域确定的标签空间。 为Twitter数据集提供的示例性结果表明该方法识别有意义的隐藏主题，并提供特定推文的有用分类。

公开(公告)号：US08145619B2

公开(公告)日：2012-03-27

申请号：US12028877

申请日：2008-02-11

申请人： Timothy R. Bowden ,  Upendra Chitnis ,  Ildar K. Khabibrakhmanov ,  Richard D. Lawrence ,  Yan Liu ,  Prem Melville

发明人： Timothy R. Bowden ,  Upendra Chitnis ,  Ildar K. Khabibrakhmanov ,  Richard D. Lawrence ,  Yan Liu ,  Prem Melville

IPC分类号： G06F7/00 ,  G06F17/30 ,  G06F13/14

CPC分类号： G06F17/30864

摘要： 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.

摘要翻译： 一种用于识别具有特定业务目标的公司的方法，其中包括使用公司隐性数据的现有来源来识别广泛的公司和相关网站，爬行与所识别的公司相关联的网站，并为每个被识别的公司索引网站内容 具体的业务目标来实现索引的Web内容。 该方法还包括使用业务目标公共标识符将公司隐含数据与索引的网页内容相加，以生成连接的结构化地图数据和索引的网页内容的存储，以及呈现连接的结构化地图数据和索引网的存储的显示图像表示 用户评论内容。 显示图像还接收用户输入，以对其中识别的每个所述公司进行评分，并使用搜索界面，查询记分，结合的结构化数据和索引的web内容的存储。 该方法还包括利用预测性机器倾斜过程增强搜索接口或来自查询的搜索结果，其允许快速识别可能在查询中遗漏的公司。

公开(公告)号：US07139733B2

公开(公告)日：2006-11-21

申请号：US10120407

申请日：2002-04-12

申请人： Heng Cao ,  Roger R. Gung ,  Yunhee Jang ,  Richard D. Lawrence ,  Grace Lin ,  Yingdong Lu

发明人： Heng Cao ,  Roger R. Gung ,  Yunhee Jang ,  Richard D. Lawrence ,  Grace Lin ,  Yingdong Lu

IPC分类号： G06Q40/00

CPC分类号： G06Q30/08 ,  G06Q40/04

摘要： 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.

公开(公告)号：US08909643B2

公开(公告)日：2014-12-09

申请号：US13315798

申请日：2011-12-09

申请人： Saha Ankan ,  Arindam Banerjee ,  Shiva P. Kasiviswanathan ,  Richard D. Lawrence ,  Prem Melville ,  Vikas Sindhwani ,  Edison L. Ting

发明人： Saha Ankan ,  Arindam Banerjee ,  Shiva P. Kasiviswanathan ,  Richard D. Lawrence ,  Prem Melville ,  Vikas Sindhwani ,  Edison L. Ting

IPC分类号： G06F17/30

CPC分类号： G06F17/2785 ,  G06F17/30619

摘要： 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.

摘要翻译： 一套用于推断主题演变和出现在一组文件中的方法，系统和计算机程序产品。 在一个实施例中，该方法包括使用文档中的文本形成一组矩阵，并且分析这些矩阵以将第一组主题识别为演变主题，将第二组主题识别为新兴主题。 矩阵包括识别每个文档中的多个单词的第一矩阵X，标识每个文档中的众多主题的第二矩阵W，以及为每个主题中的每一个标识多个单词的第三矩阵H 。 对这些矩阵进行分析，以确定不断发展的新兴主题。 在一个实施例中，文档形成流数据集，并且使用两种形式的时间规则化器来帮助识别流数据集中不断发展的主题和新兴主题。

公开(公告)号：US20130018828A1

公开(公告)日：2013-01-17

申请号：US13619059

申请日：2012-09-14

申请人： Jingrui He ,  Richard D. Lawrence ,  Prem Melville ,  Vikas Sindhwani ,  Vijil E. Chenthamarakshan

发明人： Jingrui He ,  Richard D. Lawrence ,  Prem Melville ,  Vikas Sindhwani ,  Vijil E. Chenthamarakshan

IPC分类号： G06N5/02 ,  G06F15/18

CPC分类号： G06N5/022 ,  G06F16/353 ,  G06F16/367

摘要： 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.

公开(公告)号：US20110251877A1

公开(公告)日：2011-10-13

申请号：US12755836

申请日：2010-04-07

申请人： Richard D. Lawrence ,  Claudia Perlich

发明人： Richard D. Lawrence ,  Claudia Perlich

IPC分类号： G06Q10/00 ,  G06Q90/00 ,  G06F17/30

CPC分类号： G06Q10/06375 ,  G06Q10/0639 ,  G06Q30/012 ,  G06Q30/02

摘要： 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.

摘要翻译： 影响分析模型确定产品中零件清除的影响。 实体是包括多个子组件的标识。 识别与该实体相关联的一个或多个性能测量。 识别要从实体中移除的一个或多个子组件。 定义替代冲击函数。 使用替代影响函数确定对所述一个或多个性能度量的影响。