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1. 发明授权

US08417650B2 Event prediction in dynamic environments 有权
标题翻译：动态环境中的事件预测
公开(公告)号：US08417650B2
公开(公告)日：2013-04-09
申请号：US12694485
申请日：2010-01-27
申请人： Thore Graepel , Joaquin Quinonero Candela , Thomas Ivan Borchert , Ralf Herbrich
发明人： Thore Graepel , Joaquin Quinonero Candela , Thomas Ivan Borchert , Ralf Herbrich
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。

2. 发明申请

US20110184778A1 Event Prediction in Dynamic Environments 有权
标题翻译：动态环境中的事件预测
公开(公告)号：US20110184778A1
公开(公告)日：2011-07-28
申请号：US12694485
申请日：2010-01-27
申请人： Thore Graepel , Joaquin Quinonero Candela , Thomas Ivan Borchert , Ralf Herbrich
发明人： Thore Graepel , Joaquin Quinonero Candela , Thomas Ivan Borchert , Ralf Herbrich
IPC分类号： G06Q30/00 , G06F17/18 , G06N5/02 , G06Q10/00
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。

3. 发明申请

US20100262568A1 Scalable Clustering 有权
标题翻译：可扩展聚类
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
IPC分类号： G06N5/02 , 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。

4. 发明申请

US20090043593A1 Event Prediction 审中-公开
标题翻译：事件预测
公开(公告)号：US20090043593A1
公开(公告)日：2009-02-12
申请号：US11835985
申请日：2007-08-08
申请人： Ralf Herbrich , Thore Graepel , Onno Zoeter , Joaquin Quinonero Candela , Phillip Trelford
发明人： Ralf Herbrich , Thore Graepel , Onno Zoeter , Joaquin Quinonero Candela , Phillip Trelford
IPC分类号： G06F17/18 , G06Q99/00
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。

5. 发明申请

US20110320767A1 Parallelization of Online Learning Algorithms 有权
标题翻译：在线学习算法的并行化
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。

6. 发明授权

US08904149B2 Parallelization of online learning algorithms 有权
标题翻译：在线学习算法的并行化
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。

7. 发明授权

US08250003B2 Computationally efficient probabilistic linear regression 有权
标题翻译：计算效率概率线性回归
公开(公告)号：US08250003B2
公开(公告)日：2012-08-21
申请号：US12209621
申请日：2008-09-12
申请人： Joaquin Quinonero Candela , Edward Lloyd Snelson , Olliver Michael Christian Williams
发明人： Joaquin Quinonero Candela , Edward Lloyd Snelson , Olliver Michael Christian Williams
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： A computationally efficient method of performing probabilistic linear regression is described. In an embodiment, the method involves adding a white noise term to a weighted linear sum of basis functions and then normalizing the combination. This generates a linear model comprising a set of sparse, normalized basis functions and a modulated noise term. When using the linear model to perform linear regression, the modulated noise term increases the variance associated with output values which are distant from any data points.
摘要翻译：描述了执行概率线性回归的计算有效的方法。在一个实施例中，该方法包括将白噪声项加到基函数的加权线性和中，然后对该组合进行归一化。这产生包括一组稀疏，归一化基函数和调制噪声项的线性模型。当使用线性模型进行线性回归时，调制噪声项增加与远离任何数据点的输出值相关联的方差。

8. 发明申请

US20100070435A1 Computationally Efficient Probabilistic Linear Regression 有权
标题翻译：计算效率概率线性回归
公开(公告)号：US20100070435A1
公开(公告)日：2010-03-18
申请号：US12209621
申请日：2008-09-12
申请人： Joaquin Quinonero Candela , Edward Lloyd Snelson , Oliver Michael Williams
发明人： Joaquin Quinonero Candela , Edward Lloyd Snelson , Oliver Michael Williams
IPC分类号： G06F15/18 , G06N7/02
CPC分类号： G06N99/005
摘要： A computationally efficient method of performing probabilistic linear regression is described. In an embodiment, the method involves adding a white noise term to a weighted linear sum of basis functions and then normalizing the combination. This generates a linear model comprising a set of sparse, normalized basis functions and a modulated noise term. When using the linear model to perform linear regression, the modulated noise term increases the variance associated with output values which are distant from any data points.
摘要翻译：描述了执行概率线性回归的计算有效的方法。在一个实施例中，该方法包括将白噪声项加到基函数的加权线性和中，然后对该组合进行归一化。这产生包括一组稀疏，归一化基函数和调制噪声项的线性模型。当使用线性模型进行线性回归时，调制噪声项增加与远离任何数据点的输出值相关联的方差。

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