公开(公告)号：US20110231356A1

公开(公告)日：2011-09-22

申请号：US12829241

申请日：2010-07-01

申请人： Akhileswar Ganesh Vaidyanathan ,  Eric N. Jean ,  Mani Thomas ,  David Louis Hample ,  Michael Thomas McGowan ,  Jijun Wang ,  Eli T. Faulkner ,  Jay Dee Askren ,  Albert Josef Boehmler ,  Durban A. Frazer

发明人： Akhileswar Ganesh Vaidyanathan ,  Eric N. Jean ,  Mani Thomas ,  David Louis Hample ,  Michael Thomas McGowan ,  Jijun Wang ,  Eli T. Faulkner ,  Jay Dee Askren ,  Albert Josef Boehmler ,  Durban A. Frazer

IPC分类号： G06N5/02

CPC分类号： G06N7/005

摘要： The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.

摘要翻译： 本发明涉及一种从直接从数据构建的图形模型自动生成假设的方法。 本发明的方法链接三个关键的科学概念，以便从数据驱动的假设模型中产生假设：包括使用基于信息理论的度量来识别数据内的信息特征子集; 从第一步识别的信息数据子集自动生成图形模型; 并将优化方法应用于图形模型以实现假设生成。 这三个概念的集成可以实现从大型复杂数据环境生成假设的可扩展方法。 使用图形模型作为模型表示可以将先验知识有效地整合到建模环境中。

公开(公告)号：US20120185424A1

公开(公告)日：2012-07-19

申请号：US12862657

申请日：2010-08-24

申请人： Akhileswar Ganesh Vaidyanathan ,  Eric N. Jean ,  Mani Thomas ,  David Louis Hample ,  Michael Thomas McGowan ,  Jijun Wang ,  Eli T. Faulkner ,  Jay Dee Askren ,  Albert Josef Boehmler ,  Durban A. Frazer

发明人： Akhileswar Ganesh Vaidyanathan ,  Eric N. Jean ,  Mani Thomas ,  David Louis Hample ,  Michael Thomas McGowan ,  Jijun Wang ,  Eli T. Faulkner ,  Jay Dee Askren ,  Albert Josef Boehmler ,  Durban A. Frazer

IPC分类号： G06N5/02

CPC分类号： G06N7/005

摘要： The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.

摘要翻译： 本发明涉及一种从直接从数据构建的图形模型自动生成假设的方法。 本发明的方法链接三个关键的科学概念，以便从数据驱动的假设模型中产生假设：包括使用基于信息理论的度量来识别数据内的信息特征子集; 从第一步识别的信息数据子集自动生成图形模型; 并将优化方法应用于图形模型以实现假设生成。 这三个概念的集成可以实现从大型复杂数据环境生成假设的可扩展方法。 使用图形模型作为模型表示可以将先验知识有效地整合到建模环境中。