公开(公告)号：US20120158791A1

公开(公告)日：2012-06-21

申请号：US12975177

申请日：2010-12-21

申请人： Gjergji Kasneci ,  David Hector Stern ,  Thore Kurt Hartwig Graepel ,  Ralf Herbrich

发明人： Gjergji Kasneci ,  David Hector Stern ,  Thore Kurt Hartwig Graepel ,  Ralf Herbrich

IPC分类号： G06F17/30

CPC分类号： G06F16/9024

摘要： Feature vector construction techniques are described. In one or more implementations, an input is received at a computing device that describes a graph query that specifies one of a plurality of entities to be used to query a knowledge base graph that represents the plurality of entities. A feature vector is constructed, by the computing device, having a number of indicator variables, each of which indicates observance of a sub-graph feature represented by a respective indicator variable in the knowledge base graph.

摘要翻译： 描述特征向量构造技术。 在一个或多个实现中，在描述指定用于查询表示多个实体的知识库的多个实体中的一个实体的图形查询的计算设备处接收输入。 由计算装置构建特征向量，其具有多个指示符变量，每个指标变量表示在知识库中由各个指示符变量表示的子图特征的遵循。

公开(公告)号：US20110320767A1

公开(公告)日：2011-12-29

申请号：US12822918

申请日：2010-06-24

申请人： Taha Bekir Eren ,  Oleg Isakov ,  Weizhu Chen ,  Jeffrey Scott Dunn ,  Thomas Ivan Borchert ,  Joaquin Quinonero Candela ,  Thore Kurt Hartwig Graepel ,  Ralf Herbrich

发明人： Taha Bekir Eren ,  Oleg Isakov ,  Weizhu Chen ,  Jeffrey Scott Dunn ,  Thomas Ivan Borchert ,  Joaquin Quinonero Candela ,  Thore Kurt Hartwig Graepel ,  Ralf Herbrich

IPC分类号： G06F15/76 ,  G06F9/02

CPC分类号： G06N99/005

摘要： Methods, systems, and media are provided for a dynamic batch strategy utilized in parallelization of online learning algorithms. The dynamic batch strategy provides a merge function on the basis of a threshold level difference between the original model state and an updated model state, rather than according to a constant or pre-determined batch size. The merging includes reading a batch of incoming streaming data, retrieving any missing model beliefs from partner processors, and training on the batch of incoming streaming data. The steps of reading, retrieving, and training are repeated until the measured difference in states exceeds a set threshold level. The measured differences which exceed the threshold level are merged for each of the plurality of processors according to attributes. The merged differences which exceed the threshold level are combined with the original partial model states to obtain an updated global model state.

摘要翻译： 提供了方法，系统和媒体，用于在线学习算法并行化中使用的动态批处理策略。 动态批量策略基于原始模型状态和更新的模型状态之间的阈值水平差，而不是根据常数或预定的批量大小来提供合并功能。 合并包括读取一批传入的流式传输数据，从合作伙伴处理器中检索任何丢失的模型信念，以及对批量的传入流数据进行培训。 重复读取，检索和训练的步骤，直到测量的状态差异超过设定的阈值水平。 根据属性对多个处理器中的每一个合并超过阈值水平的测量差异。 将超过阈值水平的合并差异与原始部分模型状态相结合以获得更新的全局模型状态。

公开(公告)号：US08904149B2

公开(公告)日：2014-12-02

申请号：US12822918

申请日：2010-06-24

申请人： Taha Bekir Eren ,  Oleg Isakov ,  Weizhu Chen ,  Jeffrey Scott Dunn ,  Thomas Ivan Borchert ,  Joaquin Quinonero Candela ,  Thore Kurt Hartwig Graepel ,  Ralf Herbrich

发明人： Taha Bekir Eren ,  Oleg Isakov ,  Weizhu Chen ,  Jeffrey Scott Dunn ,  Thomas Ivan Borchert ,  Joaquin Quinonero Candela ,  Thore Kurt Hartwig Graepel ,  Ralf Herbrich

IPC分类号： G06F15/76 ,  G06F9/02 ,  G06N99/00

CPC分类号： G06N99/005

摘要： Methods, systems, and media are provided for a dynamic batch strategy utilized in parallelization of online learning algorithms. The dynamic batch strategy provides a merge function on the basis of a threshold level difference between the original model state and an updated model state, rather than according to a constant or pre-determined batch size. The merging includes reading a batch of incoming streaming data, retrieving any missing model beliefs from partner processors, and training on the batch of incoming streaming data. The steps of reading, retrieving, and training are repeated until the measured difference in states exceeds a set threshold level. The measured differences which exceed the threshold level are merged for each of the plurality of processors according to attributes. The merged differences which exceed the threshold level are combined with the original partial model states to obtain an updated global model state.

摘要翻译： 提供了方法，系统和媒体，用于在线学习算法并行化中使用的动态批处理策略。 动态批量策略基于原始模型状态和更新的模型状态之间的阈值水平差，而不是根据常数或预定的批量大小来提供合并功能。 合并包括读取一批传入的流式传输数据，从合作伙伴处理器中检索任何丢失的模型信念，以及对批量的传入流数据进行培训。 重复读取，检索和训练的步骤，直到测量的状态差异超过设定的阈值水平。 根据属性对多个处理器中的每一个合并超过阈值水平的测量差异。 将超过阈值水平的合并差异与原始部分模型状态相结合以获得更新的全局模型状态。

公开(公告)号：US20110218946A1

公开(公告)日：2011-09-08

申请号：US12717109

申请日：2010-03-03

申请人： David Stern ,  Ralf Herbrich ,  Milad Shokouhi ,  Thore Kurt Hartwig Graepel

发明人： David Stern ,  Ralf Herbrich ,  Milad Shokouhi ,  Thore Kurt Hartwig Graepel

IPC分类号： G06F17/30 ,  G06F15/18

CPC分类号： H04L12/1859 ,  G06F16/5866 ,  G06N20/00 ,  G06Q30/02 ,  G06Q50/01

摘要： A user may request a presentation of a content item set, such as a social network comprising a set of status messages or an image database comprising a set of images. However, the volume and diversity of content items of the content item set may reduce the interest of the user in the presented content items. The potential interest of the user in the presented content items may be improved by selecting content items that are associated with one or more topics of potential interest to the user, and having a positive trending popularity among users of the content item set. Moreover, the interaction of the user with a presented content item may be monitored and used to determine the interest of the user in the topics associated with the presented content item and the popularity of the content item.

摘要翻译： 用户可以请求内容项目集的呈现，诸如包括一组状态消息的社交网络或包括一组图像的图像数据库。 然而，内容项目集的内容项目的数量和多样性可能降低用户在所呈现的内容项中的兴趣。 可以通过选择与用户潜在兴趣的一个或多个主题相关联的内容项，并且在内容项集合的用户中具有积极的趋势流行度来改善用户对所呈现的内容项目的潜在兴趣。 此外，可以监视用户与所呈现的内容项目的交互并用于确定用户在与所呈现的内容项目相关联的主题以及内容项目的受欢迎程度中的兴趣。