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    Predictive modeling accuracy
    1.
    发明授权
    Predictive modeling accuracy 有权
    预测建模精度

    公开(公告)号：US08843427B1

    公开(公告)日：2014-09-23

    申请号：US13195349

    申请日：2011-08-01

    申请人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    发明人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    IPC分类号： G06F15/18 ,  G06N3/08

    CPC分类号： G06N99/005

    摘要： In general, a method includes receiving a training data set that includes a plurality of examples, wherein each example includes one or more features and an answer, generating a plurality of modified training data sets by applying one or more filters to the training data set, each of the plurality of modified training data sets being based on a different combination of the one or more filters, training a plurality of predictive models, each of the plurality of predictive models being trained using a different modified training data set of the plurality of modified training data sets, determining a respective accuracy for each of the plurality of predictive models, identifying a most accurate predictive model based on the determined accuracies, and specifying an association between the training data set and the combination of filters used to generate the modified training data set that was used to train the most accurate predictive model.

    摘要翻译： 通常，一种方法包括接收包括多个示例的训练数据集，其中每个示例包括一个或多个特征和答案，通过向训练数据集施加一个或多个过滤器来生成多个修改的训练数据集， 所述多个修改的训练数据集中的每一个基于所述一个或多个过滤器的不同组合，训练多个预测模型，所述多个预测模型中的每一个被训练使用所述多个修改的训练数据集的不同修改训练数据集 训练数据集，确定所述多个预测模型中的每一个的相应精度，基于所确定的精度来识别最准确的预测模型，以及指定所述训练数据集与用于生成修改的训练数据的滤波器的组合之间的关联 用于训练最准确的预测模型。

    Suggesting training examples
    2.
    发明授权
    Suggesting training examples 有权
    建议培训实例

    公开(公告)号：US08606728B1

    公开(公告)日：2013-12-10

    申请号：US13228365

    申请日：2011-09-08

    申请人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    发明人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    IPC分类号： G06F15/18 ,  G06N99/00 ,  G06K9/62

    CPC分类号： G06N99/005 ,  G06K9/6256

    摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for suggesting training examples. In one aspect, a method includes receiving a plurality of training examples. A plurality of different types of predictive models are trained using the received training examples, wherein each of the predictive models implements a different machine learning technique. The performance of each trained model is measured. A suggestion score is computed for each training example according to each respective trained model, including weighting each suggestion score by the measured performance of the respective trained model. The computed suggestion scores for each training example are combined to compute an overall suggestion score for each training example, and the training examples are ranked by suggestion scores.

    摘要翻译： 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，用于建议训练示例。 一方面，一种方法包括接收多个训练示例。 使用所接收的训练示例来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。 测量每个训练模型的性能。 根据每个相应的训练模型，针对每个训练示例计算建议得分，包括通过相应训练模型的测量性能对每个建议得分加权。 将每个训练样本的计算建议得分合并计算每个训练样本的总体建议得分，并通过建议得分对训练样本进行排名。

    Predictive analytical model matching
    3.
    发明授权
    Predictive analytical model matching 有权
    预测分析模型匹配

    公开(公告)号：US08311967B1

    公开(公告)日：2012-11-13

    申请号：US13246229

    申请日：2011-09-27

    申请人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    发明人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    IPC分类号： G06F15/00 ,  G06F15/18

    CPC分类号： G06N99/005

    摘要： Methods, systems, and apparatus, for selecting a trained predictive models. A request is received from a client-subscriber computing system for access to a trained predictive model that can generate a predictive output in response to receiving input data having one or more input types. Information that describes each of the trained predictive models in a predictive model repository can be used to determine that one or more models included in the repository match the request. Determining a match can be based (at least in part) on a comparison of the one or more input types to input types included in the information that describes the trained predictive models. Access is provided to at least one of the models to the client-subscriber computing system. The models that match the request are models that were trained using training data provided by a computing system other than the client-subscriber computing system.

    摘要翻译： 用于选择训练有素的预测模型的方法，系统和装置。 从客户端 - 用户计算系统接收到用于访问经过训练的预测模型的请求，该预测模型可以响应于接收到具有一个或多个输入类型的输入数据而产生预测输出。 描述预测模型存储库中每个经过训练的预测模型的信息可用于确定包含在存储库中的一个或多个模型与请求匹配。 可以基于（至少部分地）将一个或多个输入类型与描述训练的预测模型的信息中包括的输入类型进行比较来确定匹配。 向客户端 - 用户计算系统提供至少一个模型的访问。 与请求相匹配的模型是使用除了客户端 - 用户计算系统之外的计算系统提供的训练数据训练的模型。

    Predictive analytical model matching
    4.
    发明授权
    Predictive analytical model matching 有权
    预测分析模型匹配

    公开(公告)号：US08521664B1

    公开(公告)日：2013-08-27

    申请号：US13172714

    申请日：2011-06-29

    申请人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    发明人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    IPC分类号： G06F15/18

    CPC分类号： G06N99/005

    摘要： Methods, systems, and apparatus, for selecting a trained predictive models. A request is received from a client-subscriber computing system for access to a trained predictive model that can generate a predictive output in response to receiving input data having one or more input types. Information that describes each of the trained predictive models in a predictive model repository can be used to determine that one or more models included in the repository match the request. Determining a match can be based (at least in part) on a comparison of the one or more input types to input types included in the information that describes the trained predictive models. Access is provided to at least one of the models to the client-subscriber computing system. The models that match the request are models that were trained using training data provided by a computing system other than the client-subscriber computing system.

    摘要翻译： 用于选择训练有素的预测模型的方法，系统和装置。 从客户端 - 用户计算系统接收到用于访问经过训练的预测模型的请求，该预测模型可以响应于接收到具有一个或多个输入类型的输入数据而产生预测输出。 描述预测模型存储库中每个经过训练的预测模型的信息可用于确定包含在存储库中的一个或多个模型与请求匹配。 可以基于（至少部分地）将一个或多个输入类型与描述训练的预测模型的信息中包括的输入类型进行比较来确定匹配。 向客户端 - 用户计算系统提供至少一个模型的访问。 与请求相匹配的模型是使用由客户端 - 用户计算系统以外的计算系统提供的训练数据训练的模型。

    Predictive analytical modeling for databases
    5.
    发明授权
    Predictive analytical modeling for databases 有权
    数据库的预测分析建模

    公开(公告)号：US08443013B1

    公开(公告)日：2013-05-14

    申请号：US13246541

    申请日：2011-09-27

    申请人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    发明人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    IPC分类号： G06F7/00 ,  G06F17/30

    CPC分类号： G06Q10/04 ,  G06F17/30477 ,  G06N99/005

    摘要： A computer-implemented method includes obtaining a database table, the table including multiple rows and multiple columns, in which one or more rows are missing at least one column value, executing a script, using a script engine, in response to obtaining the table, in which executing the script causes one or more values from the rows to be provided as input data to a first predictive model, and processing, using the first predictive model, the input data to obtain output data, the output data including a predicted value for at least one of the missing column values, and populating one or more of the missing column values with the output data to provide a revised database table.

    摘要翻译： 计算机实现的方法包括：获得数据库表，所述表包括多行和多列，其中一行或多行缺少至少一个列值，响应于获得该表而使用脚本引擎执行脚本， 其中执行所述脚本使得所述行中的一个或多个值被提供为第一预测模型的输入数据，并且使用所述第一预测模型处理所述输入数据以获得输出数据，所述输出数据包括 至少一个缺少的列值，并使用输出数据填充一个或多个缺失的列值，以提供修订的数据库表。

    Predictive analytical model selection
    7.
    发明授权
    Predictive analytical model selection 有权
    预测分析模型选择

    公开(公告)号：US08694540B1

    公开(公告)日：2014-04-08

    申请号：US13246410

    申请日：2011-09-27

    申请人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    发明人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    IPC分类号： G06F17/30

    CPC分类号： G06F17/30292

    摘要： A computer-implemented method includes obtaining a database table, the database table including data arranged in a plurality of rows and a plurality of columns, each column of data being associated with a different tag that specifies a category for data in the column, using one or more processors to identify a first predictive model, from a collection of predictive models, that can be applied to the database table to generate a predictive output, in which identifying the first predictive model is based on one or more of the different tags, adding a name associated with the first predictive model to a set of names of predictive models that are compatible with the database table, and providing the set of names of predictive models to a client device.

    摘要翻译： 计算机实现的方法包括获得数据库表，数据库表包括以多行和多列排列的数据，每列数据与指定列中的数据的类别的不同标签相关联，使用一个 或多个处理器，以从预测模型集合中识别可以应用于数据库表以产生预测输出的第一预测模型，其中识别第一预测模型基于一个或多个不同标签，添加 与第一预测模型相关联的名称与与数据库表兼容的预测模型的一组名称，以及将预测模型的名称集合提供给客户端设备。

    Multi-label modeling using a plurality of classifiers
    10.
    发明授权
    Multi-label modeling using a plurality of classifiers 有权
    使用多个分类器进行多标签建模

    公开(公告)号：US08706656B1

    公开(公告)日：2014-04-22

    申请号：US13218623

    申请日：2011-08-26

    申请人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    发明人： Wei-Hao Lin ,  Travis H. K. Green ,  Robert Kaplow ,  Gang Fu ,  Gideon S. Mann

    IPC分类号： G06F15/18 ,  G06F17/00 ,  G06N99/00

    CPC分类号： G06N99/005

    摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for multi-label models. One of the methods includes receiving training records, each training record having an input, a first output, and a second output. The method includes generating a first classifier using as input one of the inputs and using as output a corresponding one of the first outputs. The method includes generating a second classifier using as input one of the inputs and using as output a corresponding one of the second outputs. The method includes inputting the inputs into the first classifier and generating first predictive outputs. The method includes inputting the inputs into the second classifier and generating second predictive outputs. The method also includes generating a third classifier using as input the first output and the second output and using as output the first output and the second output of the corresponding training record.

    摘要翻译： 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，用于多标签模型。 方法之一包括接收训练记录，每个训练记录具有输入，第一输出和第二输出。 该方法包括使用输入中的一个输入并使用第一输出中相应的一个作为输出来产生第一分类器。 该方法包括使用输入中的一个输入并使用第二输出中相应的一个作为输出来生成第二分类器。 该方法包括将输入输入到第一分类器并产生第一预测输出。 该方法包括将输入输入到第二分类器并产生第二预测输出。 该方法还包括使用第一输出和第二输出作为输入产生第三分类器，并且使用相应训练记录的第一输出和第二输出作为输出。