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公开(公告)号：US20100262568A1
公开(公告)日：2010-10-14
申请号：US12421853
申请日：2009-04-10
申请人： Anton Schwaighofer , Joaquin Quinonero Candela , Thomas Borchert , Thore Graepel , Ralf Herbrich
发明人： Anton Schwaighofer , Joaquin Quinonero Candela , Thomas Borchert , Thore Graepel , Ralf Herbrich
CPC分类号： G06N99/005 , G06K9/6226
摘要： A scalable clustering system is described. In an embodiment the clustering system is operable for extremely large scale applications where millions of items having tens of millions of features are clustered. In an embodiment the clustering system uses a probabilistic cluster model which models uncertainty in the data set where the data set may be for example, advertisements which are subscribed to keywords, text documents containing text keywords, images having associated features or other items. In an embodiment the clustering system is used to generate additional features for associating with a given item. For example, additional keywords are suggested which an advertiser may like to subscribe to. The additional features that are generated have associated probability values which may be used to rank those features in some embodiments. User feedback about the generated features is received and used to revise the feature generation process in some examples.
摘要翻译： 描述了可扩展的集群系统。 在一个实施例中，聚类系统可操作用于具有数千万个特征的数百万个项目被聚集的极大规模应用。 在一个实施例中，聚类系统使用概率聚类模型，其对数据集中的不确定性进行建模，其中数据集可以是例如订阅关键字的广告，包含文本关键字的文本文档，具有相关联特征或其他项目的图像。 在一个实施例中，聚类系统用于产生用于与给定项目相关联的附加特征。 例如，建议广告客户可能希望订阅的其他关键字。 生成的附加特征具有相关联的概率值，其可用于在某些实施例中对这些特征进行排名。 在一些示例中，接收并用于用户对生成的特征的反馈以修改特征生成过程。
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公开(公告)号：US08204838B2
公开(公告)日：2012-06-19
申请号：US12421853
申请日：2009-04-10
申请人： Anton Schwaighofer , Joaquin Quiñonero Candela , Thomas Borchert , Thore Graepel , Ralf Herbrich
发明人： Anton Schwaighofer , Joaquin Quiñonero Candela , Thomas Borchert , Thore Graepel , Ralf Herbrich
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06K9/6226
摘要： A scalable clustering system is described. In an embodiment the clustering system is operable for extremely large scale applications where millions of items having tens of millions of features are clustered. In an embodiment the clustering system uses a probabilistic cluster model which models uncertainty in the data set where the data set may be for example, advertisements which are subscribed to keywords, text documents containing text keywords, images having associated features or other items. In an embodiment the clustering system is used to generate additional features for associating with a given item. For example, additional keywords are suggested which an advertiser may like to subscribe to. The additional features that are generated have associated probability values which may be used to rank those features in some embodiments. User feedback about the generated features is received and used to revise the feature generation process in some examples.
摘要翻译： 描述了可扩展的集群系统。 在一个实施例中，聚类系统可操作用于具有数千万个特征的数百万个项目被聚集的极大规模应用。 在一个实施例中，聚类系统使用概率聚类模型，其对数据集中的不确定性进行建模，其中数据集可以是例如订阅关键字的广告，包含文本关键字的文本文档，具有相关联特征或其他项目的图像。 在一个实施例中，聚类系统用于产生用于与给定项目相关联的附加特征。 例如，建议广告客户可能希望订阅的其他关键字。 生成的附加特征具有相关联的概率值，其可用于在某些实施例中对这些特征进行排名。 在一些示例中，接收并用于用户对生成的特征的反馈以修改特征生成过程。
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公开(公告)号：US20090043593A1
公开(公告)日：2009-02-12
申请号：US11835985
申请日：2007-08-08
CPC分类号： G06Q10/04 , G06Q30/0185
摘要： There are many situations in which it is desired to predict outcomes of events. In an example, an event prediction system is described which receives variables for a proposed event. The system accesses learnt statistics describing belief about weights associated with the variables and uses the weights to determine probability information that the proposed event will have a specified outcome. The process involves combining the accessed statistics and mapping them into a number representing the probability. In another example, a machine learning process using assumed density filtering is used to learn the statistics from data about observed events. The event prediction system may be used as part of any suitable type of system such as an internet advertising system, an email filtering system, or a fraud detection system.
摘要翻译： 有很多情况需要预测事件的结果。 在一个示例中，描述了接收所提出事件的变量的事件预测系统。 系统访问学习的统计数据，描述与变量相关联的权重的信念，并使用权重来确定拟议事件将具有指定结果的概率信息。 该过程涉及组合所访问的统计数据并将其映射成表示概率的数字。 在另一个例子中，使用假设浓度滤波的机器学习过程来从关于观测事件的数据中学习统计数据。 事件预测系统可以用作任何合适类型的系统的一部分，例如互联网广告系统，电子邮件过滤系统或欺诈检测系统。
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公开(公告)号：US08417650B2
公开(公告)日：2013-04-09
申请号：US12694485
申请日：2010-01-27
IPC分类号： G06F15/18
CPC分类号： G06Q30/02 , G06N7/005 , G06Q10/00 , G06Q30/0202
摘要： Event prediction in dynamic environments is described. In an embodiment a prediction engine may use the learnt information to predict events in order to control a system such as for internet advertising, email filtering, fraud detection or other applications. In an example one or more variables exists for pre-specified features describing or associated with events and each variable is considered to have an associated weight and time stamp. For example, belief about each weight is represented using a probability distribution and a dynamics process is used to modify the probability distribution in a manner dependent on the time stamp for that weight. For example, the uncertainty about the associated variable's influence on prediction of future events is increased. Examples of different schedules for applying the dynamics process are given.
摘要翻译： 描述动态环境中的事件预测。 在一个实施例中，预测引擎可以使用所学习的信息来预测事件，以便控制诸如互联网广告，电子邮件过滤，欺诈检测或其他应用的系统。 在一个示例中，存在用于描述或与事件相关联的预先指定的特征的一个或多个变量，并且每个变量被认为具有相关联的权重和时间戳。 例如，使用概率分布来表示关于每个权重的信念，并且使用动态过程以取决于该权重的时间戳的方式来修改概率分布。 例如，相关变量对未来事件预测的影响的不确定性增加。 给出了应用动态过程的不同时间表的示例。
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公开(公告)号：US20110184778A1
公开(公告)日：2011-07-28
申请号：US12694485
申请日：2010-01-27
CPC分类号： G06Q30/02 , G06N7/005 , G06Q10/00 , G06Q30/0202
摘要： Event prediction in dynamic environments is described. In an embodiment a prediction engine may use the learnt information to predict events in order to control a system such as for internet advertising, email filtering, fraud detection or other applications. In an example one or more variables exists for pre-specified features describing or associated with events and each variable is considered to have an associated weight and time stamp. For example, belief about each weight is represented using a probability distribution and a dynamics process is used to modify the probability distribution in a manner dependent on the time stamp for that weight. For example, the uncertainty about the associated variable's influence on prediction of future events is increased. Examples of different schedules for applying the dynamics process are given.
摘要翻译： 描述动态环境中的事件预测。 在一个实施例中，预测引擎可以使用所学习的信息来预测事件，以便控制诸如互联网广告，电子邮件过滤，欺诈检测或其他应用的系统。 在一个示例中，存在用于描述或与事件相关联的预先指定的特征的一个或多个变量，并且每个变量被认为具有相关联的权重和时间戳。 例如，使用概率分布来表示关于每个权重的信念，并且使用动态过程以取决于该权重的时间戳的方式来修改概率分布。 例如，相关变量对未来事件预测的影响的不确定性增加。 给出了应用动态过程的不同时间表的示例。
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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
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。
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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
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。
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公开(公告)号：US09413557B2
公开(公告)日：2016-08-09
申请号：US12818161
申请日：2010-06-18
申请人： Ralf Herbrich , Thore Graepel , Yoram Bachrach
发明人： Ralf Herbrich , Thore Graepel , Yoram Bachrach
CPC分类号： H04L12/5855 , G06Q30/0214 , G06Q30/0273 , G06Q50/01 , H04L51/14 , H04L51/32
摘要： Online recommendations are tracked through a forwarding service. The forwarding service can provide such statistics to an ad service, which can provide incentives to the recommending user and a consuming user. Example incentives may include an accumulation of points by the recommending user, a discount to the consuming user if a purchase is made in response to the recommendation, etc. To determine how much of an incentive each participant in the recommendation flow receives, a graph is created to model the recommendation flow and incentives are allocated using a cooperative game description based on this graph that associates each participant with a power index that represents that participants share of the incentive.
摘要翻译： 通过转发服务跟踪在线建议。 转发服务可以将这样的统计信息提供给广告服务，其可以向推荐用户和消费用户提供激励。 示例性激励可以包括推荐用户的积分积分，如果响应于推荐而进行购买，则对消费用户的折扣等。为了确定推荐流程中每个参与者接收到的激励多少， 创建以建模流程的模型和激励是使用基于该图的合作游戏描述来分配的，其将每个参与者与表示激励的参与者份额的权力指数相关联。
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9.发明申请公开(公告)号：US20120089581A1
公开(公告)日：2012-04-12
申请号：US12899569
申请日：2010-10-07
申请人： Anoop Gupta , Thore Graepel , Ralf Herbrich
发明人： Anoop Gupta , Thore Graepel , Ralf Herbrich
摘要： A publishing engine captures capturing commercial events and other information (collectively, “commercial information”) associated with a first user and automatically notifies other users in the social network of the first user of this commercial information. The publishing engine also notifies one or more search engines of these events and information. Based on this commercial information, the search engine can augment search results of the members of the social network to include historical notifications relating to commercial transactions for similar products and/or services by others in their social network. In this manner, for example, the search engine can provide results directing the searcher to other users in their social network who have purchased such products and/or services.
摘要翻译： 发布引擎捕获与第一用户相关联的商业事件和其他信息（统称为“商业信息”），并自动通知该商业信息的第一用户的社交网络中的其他用户。 发布引擎还通知一个或多个搜索引擎的这些事件和信息。 基于这种商业信息，搜索引擎可以增加社交网络成员的搜索结果，以包括与他们的社交网络中的其他类似产品和/或服务相关的商业交易的历史通知。 以这种方式，例如，搜索引擎可以提供将搜索者指向已经购买了这样的产品和/或服务的社交网络中的其他用户的结果。
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公开(公告)号：US20070112706A1
公开(公告)日：2007-05-17
申请号：US11607482
申请日：2006-11-30
申请人： Ralf Herbrich , Thore Graepel
发明人： Ralf Herbrich , Thore Graepel
IPC分类号： G06F15/18
CPC分类号： G07F17/3274 , G07F17/32
摘要： A skill scoring frameworks allows for handicapping an individual game player in a gaming environment in preparation of matching the game player with other game players, whether for building teams or assigning competitors, or both. By introducing handicapping into the skill scoring framework, a highly skilled player may select one or more game characteristics (e.g., a less than optimal racing vehicle, reduced character capabilities, etc.) and therefore be assigned a handicap that allows the player to be matched with lower skilled players for competitive game play. Handicaps may apply positively or negatively a player's skill score during the matching stage. Handicaps may also be updated based on the game outcomes of the game play in which they were applied.
摘要翻译： 技能评分框架允许在游戏环境中妨碍个人游戏玩家，以准备将游戏玩家与其他游戏玩家相匹配，无论是建立团队还是分配竞争对手，或两者兼有。 通过在技能评分框架中引入障碍，高技能玩家可以选择一个或多个游戏特征（例如，不太优化的赛车，减少的角色能力等），并且因此被分配允许玩家匹配的障碍 与较低技术的玩家竞争游戏。 障碍可能在比赛阶段积极或消极地运用玩家的技能得分。 还可以根据应用游戏结果的游戏结果更新障碍。
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