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    • 2. 发明申请
    • Hidden document data removal
    • 隐藏的文档数据删除
    • US20070174766A1
    • 2007-07-26
    • US11336329
    • 2006-01-20
    • Donald RubinWilliam NeumannLauren Antonoff
    • Donald RubinWilliam NeumannLauren Antonoff
    • G06F17/00
    • G06F17/24G06F17/218
    • Technology for finding and acting on hidden data contained in documents generated by a user in productivity applications is disclosed. The technology uses a user configurable document release policy file and a document inspector which parses a document file based on the configuration policy and either presents options to the user to make changes, implements changes automatically, or both, based on the policy definition. A method implemented at least in part by a computing device includes loading a user defined document policy configuration including data types identified as hidden data. A document is then parsed for the defined hidden data and a policy defined action is executed on the hidden data in the document in accordance with the document policy configuration.
    • 公开了用于在生产率应用中发现和执行由用户生成的文档中包含的隐藏数据的技术。 该技术使用用户可配置文档发布策略文件和文档检查器,该文档检查器根据配置策略解析文档文件,并根据策略定义向用户呈现选项以进行更改,自动实现更改或同时实现更改。 至少部分地由计算设备实现的方法包括加载用户定义的文档策略配置,包括被识别为隐藏数据的数据类型。 然后根据文档策略配置对文档中的隐藏数据进行解析,并对文档中的隐藏数据执行策略定义的操作。
    • 5. 发明申请
    • Dimension reduction in predictive model development
    • 预测模型开发中的维度减少
    • US20050234762A1
    • 2005-10-20
    • US10826949
    • 2004-04-16
    • Stephen PintoRichard MansfieldDonald Rubin
    • Stephen PintoRichard MansfieldDonald Rubin
    • G06F15/18G06Q30/00G06F17/60
    • G06Q30/02G06Q40/025
    • Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. Historical multi-dimensional data is received representing multiple variables to be used as an input to a predictive model of a commercial system variables are pruned for which the data is sparse or missing, and the population of variables is adjusted to represent main effects exhibited by the data and interaction and non-linear effects exhibited by the data.
    • 使用模型生成平台的各种工具和功能生成模型。 例如,关于用户根据关于正被建模的系统的历史数据生成预测模型的项目,通过图形用户界面提供要遵循的模型生成活动的结构化序列,所述序列包括维度 减少,模型生成,模型过程验证和模型重新生成。 接收到的代表多个变量的历史多维数据被用作商业系统的预测模型的输入,修剪数据稀疏或丢失的变量,并且调节变量的总数以表示由 数据和相互作用以及数据显示的非线性效应。
    • 6. 发明申请
    • Predictive model validation
    • 预测模型验证
    • US20050234753A1
    • 2005-10-20
    • US10826947
    • 2004-04-16
    • Stephen PintoRichard MansfieldMarc JacobsDonald Rubin
    • Stephen PintoRichard MansfieldMarc JacobsDonald Rubin
    • G06F19/00G06Q10/00G06Q30/00G06F17/60
    • G06Q10/04G06Q30/02G06Q40/025
    • Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. In connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is enabled to validate the model development process with cross-validation between at least two subsets of the historical data; the validated model development process is enabled to be reapplied.
    • 使用模型生成平台的各种工具和功能生成模型。 例如,关于用户根据关于正被建模的系统的历史数据生成预测模型的项目,通过图形用户界面提供要遵循的模型生成活动的结构化序列,所述序列包括维度 减少,模型生成,模型过程验证和模型重新生成。 关于用户根据关于正在建模的系统的历史数据生成预测模型的项目,用户能够通过历史数据的至少两个子集之间的交叉验证来验证模型开发过程; 验证的模型开发过程能够被重新应用。
    • 7. 发明授权
    • Predictive model validation
    • 预测模型验证
    • US08170841B2
    • 2012-05-01
    • US10826947
    • 2004-04-16
    • Stephen K. PintoRichard MansfieldMarc JacobsDonald Rubin
    • Stephen K. PintoRichard MansfieldMarc JacobsDonald Rubin
    • G06G7/60
    • G06Q10/04G06Q30/02G06Q40/025
    • Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation.In connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is enabled to validate the model development process with cross-validation between at least two subsets of the historical data; the validated model development process is enabled to be reapplied.
    • 使用模型生成平台的各种工具和功能生成模型。 例如,关于用户根据关于正被建模的系统的历史数据生成预测模型的项目,通过图形用户界面提供要遵循的模型生成活动的结构化序列,所述序列包括维度 减少,模型生成,模型过程验证和模型重新生成。 关于用户根据关于正在建模的系统的历史数据生成预测模型的项目,用户能够通过历史数据的至少两个子集之间的交叉验证来验证模型开发过程; 验证的模型开发过程能够被重新应用。
    • 10. 发明申请
    • Predictive model augmentation by variable transformation
    • 通过变量变换预测模型增加
    • US20050234763A1
    • 2005-10-20
    • US10826950
    • 2004-04-16
    • Stephen PintoRichard MansfieldMarc JacobsDonald Rubin
    • Stephen PintoRichard MansfieldMarc JacobsDonald Rubin
    • G06Q10/00G06F17/60
    • G06Q10/04G06Q30/0201
    • Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation. Historical multi-dimensional data is received representing multiple source variables to be used as an input to a predictive model of a commercial system and applying transformations to the data that are selected based on the strength of measurement represented by a variable; variables are transformed into new more predictive variables, including the Bayesian renormalization of sparsely sampled variable and including the imputation of missing values for categorical or continuous variables.
    • 使用模型生成平台的各种工具和功能生成模型。 例如,关于用户根据关于正被建模的系统的历史数据生成预测模型的项目,通过图形用户界面提供要遵循的模型生成活动的结构化序列,所述序列包括维度 减少,模型生成,模型过程验证和模型重新生成。 接收表示多个源变量的历史多维数据,以用作商业系统的预测模型的输入,并且基于由变量表示的测量强度来选择的数据进行变换; 变量被变换成新的更具预测性的变量,包括稀疏抽样变量的贝叶斯重正化,包括对分类或连续变量的缺失值的插补。