公开(公告)号：US09342798B2

公开(公告)日：2016-05-17

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: G06F15/18 ,  G06N99/00 ,  G06K9/62

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。

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。

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

IPC: G06F15/18 ,  G06N99/00

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。

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.

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。