公开(公告)号：US20090157573A1

公开(公告)日：2009-06-18

申请号：US12178553

申请日：2008-07-23

申请人： Roger N. Anderson ,  Albert Boulanger ,  David L. Waltz ,  Phil Long ,  Arias Marta ,  Philip Gross ,  Hila Becker ,  Arthur Kressner ,  Mark Mastrocinque ,  Matthew Koenig ,  John A. Johnson

发明人： Roger N. Anderson ,  Albert Boulanger ,  David L. Waltz ,  Phil Long ,  Arias Marta ,  Philip Gross ,  Hila Becker ,  Arthur Kressner ,  Mark Mastrocinque ,  Matthew Koenig ,  John A. Johnson

IPC分类号： G06F15/18

CPC分类号： G06N99/005

摘要： A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.

摘要翻译： 机器学习系统为公用事业的配电系统中的馈线电缆创建故障敏感性排名。 机器学习系统采用mart升级算法和支持向量机（SVM）算法，生成馈线故障预测模型，对静态和动态馈线属性数据进行了培训。 馈线由故障敏感性和对公用事业运营商和工程师的排名进行动态排名，以便他们可以主动为服务分配系统，以防止局部停电。 馈线排名可用于重定向电力流量并优先进行维修。 建立了一个反馈回路，以评估配电系统对优化预防性维护计划所采取的现场行动的响应。

公开(公告)号：US08886636B2

公开(公告)日：2014-11-11

申请号：US12342449

申请日：2008-12-23

申请人： Evgeniy Gabrilovich ,  Andrei Broder ,  Bo Pang ,  Vanja Josifovski ,  Hila Becker

发明人： Evgeniy Gabrilovich ,  Andrei Broder ,  Bo Pang ,  Vanja Josifovski ,  Hila Becker

IPC分类号： G06F7/00 ,  G06F17/30 ,  G06Q30/02

CPC分类号： G06F17/30731 ,  G06Q30/02

摘要： A computer-implemented method is disclosed for determining a type of landing page to which to transfer web searchers that enter a particular query, the method comprising: classifying a landing page as one of a plurality of landing page classes with a trained classifier of a computer based on textual content of the landing page; determining, by the computer, characteristics of one or more query to be associated with the landing page; and choosing, with the computer, whether to retain or to change classification of the landing page to be associated with the one or more query based on relative average conversion rates of advertisements on a plurality of manually-classified landing pages when associated with the characteristics of the one or more query.

摘要翻译： 公开了一种用于确定输入特定查询的网页搜索者的登陆页面的类型的计算机实现的方法，所述方法包括：将着陆页面分类为具有计算机的训练分类器的多个着陆页类别之一 基于着陆页的文字内容; 由计算机确定要与着陆页相关联的一个或多个查询的特征; 并且与计算机一起选择是否基于在与多个手动分类的着陆页上的广告的相对平均转换率相关联来保留或改变与一个或多个查询相关联的着陆页的分类，当与 一个或多个查询。

公开(公告)号：US07945524B2

公开(公告)日：2011-05-17

申请号：US12178553

申请日：2008-07-23

申请人： Roger N. Anderson ,  Albert Boulanger ,  David L. Waltz ,  Phil Long ,  Marta Arias ,  Philip Gross ,  Hila Becker ,  Arthur Kressner ,  Mark Mastrocinque ,  Matthew Koenig ,  John A. Johnson

发明人： Roger N. Anderson ,  Albert Boulanger ,  David L. Waltz ,  Phil Long ,  Marta Arias ,  Philip Gross ,  Hila Becker ,  Arthur Kressner ,  Mark Mastrocinque ,  Matthew Koenig ,  John A. Johnson

IPC分类号： G06F15/18

CPC分类号： G06N99/005

摘要： A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.

摘要翻译： 机器学习系统为公用事业的配电系统中的馈线电缆创建故障敏感性排名。 机器学习系统采用mart升级算法和支持向量机（SVM）算法，生成馈线故障预测模型，对静态和动态馈线属性数据进行了培训。 馈线由故障敏感性和对公用事业运营商和工程师的排名进行动态排名，以便他们可以主动为服务分配系统，以防止局部停电。 馈线排名可用于重定向电力流量并优先进行维修。 建立了一个反馈回路，以评估配电系统对优化预防性维护计划所采取的现场行动的响应。

公开(公告)号：US20100161605A1

公开(公告)日：2010-06-24

申请号：US12342449

申请日：2008-12-23

申请人： Evgeniy Gabrilovich ,  Andrei Broder ,  Bo Pang ,  Vanja Josifovski ,  Hila Becker

发明人： Evgeniy Gabrilovich ,  Andrei Broder ,  Bo Pang ,  Vanja Josifovski ,  Hila Becker

IPC分类号： G06F17/30

CPC分类号： G06F17/30731 ,  G06Q30/02

摘要： A computer-implemented method is disclosed for determining a type of landing page to which to transfer web searchers that enter a particular query, the method comprising: classifying a landing page as one of a plurality of landing page classes with a trained classifier of a computer based on textual content of the landing page; determining, by the computer, characteristics of one or more query to be associated with the landing page; and choosing, with the computer, whether to retain or to change classification of the landing page to be associated with the one or more query based on relative average conversion rates of advertisements on a plurality of manually-classified landing pages when associated with the characteristics of the one or more query.

摘要翻译： 公开了一种用于确定输入特定查询的网页搜索者的登陆页面的类型的计算机实现的方法，所述方法包括：将着陆页面分类为具有计算机的训练分类器的多个着陆页类别之一 基于着陆页的文字内容; 由计算机确定要与着陆页相关联的一个或多个查询的特征; 并且与计算机一起选择是否基于在与多个手动分类的着陆页上的广告的相对平均转换率相关联来保留或改变与一个或多个查询相关联的着陆页的分类，当与 一个或多个查询。