公开(公告)号：US09477929B2

公开(公告)日：2016-10-25

申请号：US13619142

申请日：2012-09-14

申请人： Jingrui He ,  Richard D. Lawrence ,  Yan Liu

发明人： Jingrui He ,  Richard D. Lawrence ,  Yan Liu

IPC分类号： G06F5/00 ,  G06N5/00 ,  G06N99/00

CPC分类号： G06N99/005

摘要： 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.

摘要翻译： 转移学习是利用来自某些领域的标记示例的信息来预测另一个域中的示例的标签的任务。 发现情绪预测，图像分类和网络入侵检测等丰富的实际应用。 基于图形的传输学习框架通过示例特征示例三方图将标签信息从源域传播到目标域，并通过示例性的二分图更加强调来自目标域的标记示例。 迭代算法使框架可扩展到大规模应用程序。 该框架通过原理方式的共同特征将标签信息传播到与源域无关的特征和目标域中的未标记示例。

公开(公告)号：US20130013540A1

公开(公告)日：2013-01-10

申请号：US13619142

申请日：2012-09-14

申请人： Jingrui He ,  Richard D. Lawrence ,  Yan Liu

发明人： Jingrui He ,  Richard D. Lawrence ,  Yan Liu

IPC分类号： G06F15/18

CPC分类号： G06N99/005

摘要： 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.

摘要翻译： 转移学习是利用来自某些领域的标记示例的信息来预测另一个域中的示例的标签的任务。 发现情绪预测，图像分类和网络入侵检测等丰富的实际应用。 基于图形的传输学习框架通过示例特征示例三方图将标签信息从源域传播到目标域，并通过示例性的二分图更加强调来自目标域的标记示例。 迭代算法使框架可扩展到大规模应用程序。 该框架通过原理方式的共同特征将标签信息传播到与源域无关的特征和目标域中的未标记示例。

公开(公告)号：US20110320387A1

公开(公告)日：2011-12-29

申请号：US12938063

申请日：2010-11-02

申请人： Jingrui He ,  Richard D. Lawrence ,  Yan Liu

发明人： Jingrui He ,  Richard D. Lawrence ,  Yan Liu

IPC分类号： G06F15/18

CPC分类号： G06N20/00

摘要： 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.

摘要翻译： 转移学习是利用来自某些领域的标记示例的信息来预测另一个域中的示例的标签的任务。 发现情绪预测，图像分类和网络入侵检测等丰富的实际应用。 基于图形的传输学习框架通过示例特征示例三方图将标签信息从源域传播到目标域，并通过示例性的二分图更加强调来自目标域的标记示例。 迭代算法使框架可扩展到大规模应用程序。 该框架通过原理方式的共同特征将标签信息传播到与源域无关的特征和目标域中的未标记示例。

公开(公告)号：US20090204569A1

公开(公告)日：2009-08-13

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： G06F17/30 ,  G06F17/00

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：US08856050B2

公开(公告)日：2014-10-07

申请号：US13006245

申请日：2011-01-13

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.

摘要翻译： 一种适用于对简短文件进行分类的问题的新颖的领域适应/转移学习方法，例如短文本消息，即时消息，推文。 该方法使用大量多标记示例（源域）来改善部分观察（目标域）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。

公开(公告)号：US20120185415A1

公开(公告)日：2012-07-19

申请号：US13006245

申请日：2011-01-13

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：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：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：USD997290S1

公开(公告)日：2023-08-29

申请号：US29838088

申请日：2022-05-10

申请人： Yan Liu

设计人： Yan Liu

摘要： FIG. 1 is a perspective view of a mosquito killer, showing my new design;
FIG. 2 is another perspective view thereof;
FIG. 3 is a front view thereof;
FIG. 4 is a rear view thereof;
FIG. 5 is a left side view thereof;
FIG. 6 is a right side view thereof;
FIG. 7 is a top plan view thereof; and,
FIG. 8 is a bottom plan view thereof.
The broken line showing of portions of the mosquito killer is included for the purpose of illustrating only and forms no part of the claimed design.