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公开(公告)号：US20150186800A1
公开(公告)日：2015-07-02
申请号：US14526926
申请日：2014-10-29
Applicant: Google Inc.
Inventor： Robert Kaplow , Wei-Hao Lin , Gideon S. Mann , Travis H.K. Green , Gang Fu , Robbie A. Haertel
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.
Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于使用训练数据训练多种不同类型的预测模型，其中每个预测模型实现不同的机器学习技术。 获得一个或多个权重，其中每个权重与多个示例中的答案类别相关联。 使用一个或多个权重对每个预测模型计算加权精度。
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公开(公告)号：US09070089B1
公开(公告)日：2015-06-30
申请号：US14047576
申请日：2013-10-07
Applicant: Google Inc.
Inventor： Wei-Hao Lin , Travis H. K. Green , Robert Kaplow , Gang Fu , Gideon S. Mann
CPC classification number: G06N3/08 , G06N99/005
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.
Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于获得预测模型的多个模型表示，与相应用户相关联的每个模型表示，并且表达相应的预测模型，并且为每个模型实现选择模型实现 基于与与对应的模型表示相关联的用户相关联的一个或多个系统使用属性的模型表示。
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公开(公告)号：US20130346351A1
公开(公告)日：2013-12-26
申请号：US13970791
申请日：2013-08-20
Applicant: Google Inc.
Inventor： Wei-Hao Lin , Travis H. K. Green , Robert Kaplow , Gang Fu , Gideon S. Mann
IPC: G06N3/08
CPC classification number: G06N3/08 , G06N99/005
Abstract: A system includes a computer(s) coupled to a data storage device(s) that stores a training data repository and a predictive model repository. The training data repository includes retained data samples from initial training data and from previously received data sets. The predictive model repository includes at least one updateable trained predictive model that was trained with the initial training data and retrained with the previously received data sets. A new data set is received. A richness score is assigned to each of the data samples in the set and to the retained data samples that indicates how information rich a data sample is for determining accuracy of the trained predictive model. A set of test data is selected based on ranking by richness score the retained data samples and the new data set. The trained predictive model is accuracy tested using the test data and an accuracy score determined.
Abstract translation: 系统包括耦合到存储训练数据存储库和预测模型存储库的数据存储设备的计算机。 训练数据库包括来自初始训练数据和先前接收的数据集的保留数据样本。 预测模型储存库包括至少一个可更新训练的预测模型，该预测模型用初始训练数据训练并用先前接收到的数据集重新训练。 接收到一个新的数据集。 丰富度得分被分配给集合中的每个数据样本和保留的数据样本，其指示如何丰富数据样本的信息用于确定训练的预测模型的准确性。 基于通过丰富度得分的保留数据样本和新数据集的等级来选择一组测试数据。 经过训练的预测模型使用测试数据进行精确测试，并确定精度得分。
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公开(公告)号：US20150170056A1
公开(公告)日：2015-06-18
申请号：US14295563
申请日：2014-06-04
Applicant: Google Inc.
Inventor： Jordan M. Breckenridge , Travis H. K. Green , Robert Kaplow , Wei-Hao Lin , Gideon S. Mann
IPC: G06N99/00
CPC classification number: G06N99/005 , G06F15/18 , G06K9/6227 , G06K9/6262
Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.
Abstract translation: 方法，系统和装置，包括在一个或多个计算机存储装置上编码的用于训练预测模型的计算机程序。 接收多个训练数据记录，每个训练数据记录包括输入数据部分和输出数据部分。 确定对应于训练数据的训练数据类型。 例如，可以通过将输出数据部分输入到一个或多个经过训练的预测分类器中来确定训练数据类型。 在另一个示例中，可以通过将输出数据部分与数据格式进行比较来确定训练数据类型。 基于所确定的训练数据类型，识别与确定的训练数据类型的训练数据兼容的一组训练功能。 培训数据和识别的训练功能组用于训练多个预测模型。
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公开(公告)号：US08909568B1
公开(公告)日：2014-12-09
申请号：US14196555
申请日：2014-03-04
Applicant: Google Inc.
Inventor： Gideon S. Mann , Jordan M. Breckenridge , Wei-Hao Lin
CPC classification number: G06N99/005
Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided to the client computing system.
Abstract translation: 方法，系统和装置，包括编码在一个或多个计算机存储装置上的用于训练预测模型的计算机程序。 一方面，一种方法包括通过网络接收来自客户端计算系统的预测建模训练数据。 训练数据和从培训功能库获得的多个训练功能用于训练多个预测模型。 为每个经过训练的预测模型生成分数，其中每个分数表示相应训练的预测模型的有效性的估计。 基于产生的分数，从训练的预测模型中选择第一训练的预测模型。 访问第一个训练有素的预测模型被提供给客户端计算系统。
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Patent Agency Ranking
公开(公告)号：US08706659B1
公开(公告)日：2014-04-22
申请号：US13886757
申请日：2013-05-03
Applicant: Google, Inc.
Inventor： Gideon S. Mann , Jordan M. Breckenridge , Wei-Hao Lin
IPC: G06F15/18
CPC classification number: G06N99/005
Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided to the client computing system.
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公开(公告)号：US20140046880A1
公开(公告)日：2014-02-13
申请号：US14061287
申请日：2013-10-23
Applicant: Google Inc.
Inventor： Jordan M. Breckenridge , Travis H.K. Green , Robert Kaplow , Wei-Hao Lin , Gideon S. Mann
IPC: G06N99/00
CPC classification number: G06N20/00
Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training and retraining predictive models. A series of training data sets are received and added to a training data queue. In response to a first condition being satisfied, multiple retrained predictive models are generated using the training data queue, multiple updateable trained predictive models obtained from a repository of trained predictive models, and multiple training functions. In response to a second condition being satisfied, multiple new trained predictive models are generated using the training data queue, at least some training data stored in a training data repository and training functions. The new trained predictive models include static trained predictive models and updateable trained predictive models. The repository of trained predictive models is updated with at least some of the retrained predictive models and new trained predictive models.
Abstract translation: 方法，系统和装置，包括在一个或多个计算机存储装置上编码的用于训练和重新训练预测模型的计算机程序。 一系列训练数据集被接收并添加到训练数据队列中。 响应于满足第一条件，使用训练数据队列，从已训练的预测模型的存储库获得的多个可更新训练的预测模型和多个训练功能来生成多个再训练的预测模型。 响应于满足第二条件，使用训练数据队列，存储在训练数据存储库中的至少一些训练数据和训练功能来生成多个新训练的预测模型。 新训练的预测模型包括静态训练预测模型和可更新训练预测模型。 训练有素的预测模型的存储库使用至少一些再培训的预测模型和新的训练预测模型进行更新。
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公开(公告)号：US20190147498A1
公开(公告)日：2019-05-16
申请号：US14676065
申请日：2015-04-01
Applicant: Google Inc.
Inventor： James Giles , Wei-Hao Lin
IPC: G06Q30/02
Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for increasing fill rate while maintaining average margin for a content serving system. Web properties associated with a publisher are identified, each web property including slots for inclusion of third party content, each slot having a reserve price which represents a minimum amount the publisher will accept for inclusion of the third party content in the slot when presented to viewers. Over a time period, an average margin is maintained for a serving system for the publisher. Bids that are valued at a price that is less than the reserve price plus a margin for the serving system are subsidized using a surplus account, based on accepted winning bids that are valued at a price that exceeds a sum of the reserve price plus compensation for the serving system.
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公开(公告)号：US20160307099A1
公开(公告)日：2016-10-20
申请号：US15194764
申请日：2016-06-28
Applicant: Google Inc.
Inventor： Wei-Hao Lin , Travis H. K. Green , Robert Kaplow , Gang Fu , Gideon S. Mann
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.
Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于得分归一化。 其中一种方法包括接收初始训练数据，包括初始训练记录的初始训练数据，识别输入数据作为输入的每个初始训练记录和作为输出的类别。 该方法包括使用初始训练数据和训练功能生成第一训练预测模型。 所述方法包括通过将初始训练记录的输入数据输入到第二训练预测模型来生成中间训练记录，所述第二训练预测模型使用训练功能生成，每个中间训练记录具有得分。 该方法还包括使用分数归一化训练函数和中间训练记录来生成分数归一化模型。
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公开(公告)号：US09406019B2
公开(公告)日：2016-08-02
申请号：US13757013
申请日：2013-02-01
Applicant: Google Inc.
Inventor： Wei-Hao Lin , Travis H. K. Green , Robert Kaplow , Gang Fu , Gideon S. Mann
CPC classification number: G06N5/022 , G06N5/02 , G06N99/005
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.
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