公开(公告)号：US20130159227A1

公开(公告)日：2013-06-20

申请号：US13767695

申请日：2013-02-14

Applicant: Yahoo! Inc.

Inventor： Anirban Dasgupta ,  Liang Zhang ,  Maxim Gurevich ,  Achint Oommen Thomas ,  Belle Tseng

IPC: G06N99/00

CPC classification number: G06N99/005 ,  G06N7/005

Abstract: Embodiments are directed towards clustering cookies for identifying unique mobile devices for associating activities over a network with a given mobile device. The cookies are clustered based on a Bayes Factor similarity model that is trained from cookie features of known mobile devices. The clusters may be used to determine the number of unique mobile devices that access a website. The clusters may also be used to provide targeted content to each unique mobile device.

Abstract translation: 实施例针对用于识别用于将网络上的活动与给定移动设备相关联的唯一移动设备的聚类cookie。 基于由已知移动设备的cookie特征训练的贝叶斯因子相似性模型，cookie是聚类的。 群集可用于确定访问网站的唯一移动设备的数量。 集群也可以用于向每个唯一移动设备提供有针对性的内容。

公开(公告)号：US08935194B2

公开(公告)日：2015-01-13

申请号：US13767695

申请日：2013-02-14

Applicant: Yahoo! Inc.

Inventor： Anirban Dasgupta ,  Liang Zhang ,  Maxim Gurevich ,  Achint Oommen Thomas ,  Belle Tseng

IPC: G06N5/02 ,  G06N7/00 ,  G06N99/00

CPC classification number: G06N99/005 ,  G06N7/005

Abstract: Embodiments are directed towards clustering cookies for identifying unique mobile devices for associating activities over a network with a given mobile device. The cookies are clustered based on a Bayes Factor similarity model that is trained from cookie features of known mobile devices. The clusters may be used to determine the number of unique mobile devices that access a website. The clusters may also be used to provide targeted content to each unique mobile device.

Abstract translation: 实施例针对用于识别用于将网络上的活动与给定移动设备相关联的唯一移动设备的聚类cookie。 基于由已知移动设备的cookie特征训练的贝叶斯因子相似性模型，cookie是聚类的。 群集可用于确定访问网站的唯一移动设备的数量。 集群也可以用于向每个唯一移动设备提供有针对性的内容。

公开(公告)号：US20150131900A1

公开(公告)日：2015-05-14

申请号：US14599454

申请日：2015-01-16

Applicant: Yahoo! Inc.

Inventor： Lyndon Kennedy ,  Roelof van Zwol ,  Nicolas Torzec ,  Belle Tseng

IPC: G06K9/00 ,  G06K9/46 ,  G06K9/52 ,  G06K9/62

CPC classification number: G06K9/00711 ,  G06K9/00228 ,  G06K9/46 ,  G06K9/4671 ,  G06K9/52 ,  G06K9/62 ,  G06K9/6201 ,  G06K2009/4666 ,  G06T3/40 ,  G06T11/60

Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model. The software uses a thumbnail received from an editor instead of the chosen thumbnail, if the chosen thumbnail is of insufficient quality as measured against a scoring threshold.

Abstract translation: 用于监督学习的软件在收集源图像时从每个源图像中提取一组像素级特征。 每个源图像与由编辑器创建的缩略图相关联。 该软件还为每个源图像生成一组独特的边界框。 并且软件为每个边界框计算一组区域级别的功能。 每个区域级别的特征来自于像素级特征之一的像素值的聚合。 该软件学习回归模型，使用计算的区域级功能和与源图像相关联的缩略图。 然后，软件根据回归模型的应用，从新图像中的独特边界框的集合中选择一个缩略图。 如果所选缩略图的质量不足，则根据评分阈值测量，软件将使用从编辑器接收的缩略图而不是所选的缩略图。

公开(公告)号：US20170012912A1

公开(公告)日：2017-01-12

申请号：US15263238

申请日：2016-09-12

Applicant: Yahoo! Inc.

Inventor： Sharat Narayan ,  Vishwanath Tumkur Ramarao ,  Belle Tseng ,  Markus Weimer ,  Young Maeng ,  Jyh-Shin Shue

IPC: H04L12/58 ,  H04L29/08 ,  H04L29/12

CPC classification number: H04L51/12 ,  G06F15/16 ,  G06Q10/107 ,  H04L51/046 ,  H04L61/2007 ,  H04L67/2866

Abstract: Embodiments are directed towards multi-level entity classification. An object associated with an entity is received. In one embodiment the object comprises and email and the entity comprises the IP address of a sending email server. If the entity has already been classified, as indicated by an entity classification cache, then a corresponding action is taken on the object. However, if the entity has not been classified, the entity is submitted to a fast classifier for classification. A feature collector concurrently fetches available features, including fast features and full features. The fast classifier classifies the entity based on the fast features, storing the result in the entity classification cache. Subsequent objects associated with the entity are processed based on the cached result of the fast classifier. Then, a full classifier classifies the entity based on at least the full features, storing the result in the entity classification cache.

Abstract translation: 实施例针对多级实体分类。 接收与实体相关联的对象。 在一个实施例中，对象包括和电子邮件，并且实体包括发送电子邮件服务器的IP地址。 如果实体已经被分类，如实体分类缓存所示，则对对象采取相应的动作。 但是，如果实体尚未分类，则将实体提交给快速分类器进行分类。 功能收集器同时提取可用功能，包括快速功能和完整功能。 快速分类器基于快速特征对实体进行分类，将结果存储在实体分类缓存中。 基于快速分类器的缓存结果来处理与实体相关联的后续对象。 然后，完整分类器至少基于全部特征对实体进行分类，将结果存储在实体分类缓存中。

公开(公告)号：US09177207B2

公开(公告)日：2015-11-03

申请号：US14599454

申请日：2015-01-16

Applicant: Yahoo! Inc.

Inventor： Lyndon Kennedy ,  Roelof van Zwol ,  Nicolas Torzec ,  Belle Tseng

IPC: G06K9/00 ,  G06T3/40 ,  G06T11/60 ,  G06K9/46 ,  G06K9/52 ,  G06K9/62

CPC classification number: G06K9/00711 ,  G06K9/00228 ,  G06K9/46 ,  G06K9/4671 ,  G06K9/52 ,  G06K9/62 ,  G06K9/6201 ,  G06K2009/4666 ,  G06T3/40 ,  G06T11/60

Abstract: Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model. The software uses a thumbnail received from an editor instead of the chosen thumbnail, if the chosen thumbnail is of insufficient quality as measured against a scoring threshold.

Abstract translation: 用于监督学习的软件在收集源图像时从每个源图像中提取一组像素级特征。 每个源图像与由编辑器创建的缩略图相关联。 该软件还为每个源图像生成一组独特的边界框。 并且软件为每个边界框计算一组区域级别的功能。 每个区域级别的特征来自于像素级特征之一的像素值的聚合。 该软件学习回归模型，使用计算的区域级功能和与源图像相关联的缩略图。 然后，软件根据回归模型的应用，从新图像中的独特边界框的集合中选择一个缩略图。 如果所选缩略图的质量不足，则根据评分阈值测量，软件将使用从编辑器接收的缩略图而不是所选的缩略图。