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Sparse higher-order Markov random field

机译:稀疏高阶马尔可夫随机场

摘要

Systems and methods are provided for identifying combinatorial feature interactions, including capturing statistical dependencies between categorical variables, with the statistical dependencies being stored in a computer readable storage medium. A model is selected based on the statistical dependencies using a neighborhood estimation strategy, with the neighborhood estimation strategy including generating sets of arbitrarily high-order feature interactions using at least one rule forest and optimizing one or more likelihood functions. A damped mean-field approach is applied to the model to obtain parameters of a Markov random field (MRF); a sparse high-order semi-restricted MRF is produced by adding a hidden layer to the MRF; indirect long-range dependencies between feature groups are modeled using the sparse high-order semi-restricted MRF; and a combinatorial dependency structure between variables is output.
机译:提供了用于识别组合特征相互作用的系统和方法,包括捕获分类变量之间的统计依赖性,其中统计依赖性被存储在计算机可读存储介质中。使用邻域估计策略基于统计依存关系来选择模型,其中邻域估计策略包括使用至少一个规则森林来生成任意高阶特征交互的集合并优化一个或多个似然函数。将阻尼平均场方法应用于模型,以获得马尔可夫随机场(MRF)的参数;通过向MRF添加隐藏层来生成稀疏的高阶半限制性MRF;使用稀疏高阶半限制性MRF对要素组之间的间接远程依赖关系进行建模;并且输出变量之间的组合依赖结构。

著录项

  • 公开/公告号US9183503B2

    专利类型

  • 公开/公告日2015-11-10

    原文格式PDF

  • 申请/专利权人 NEC LABORATORIES AMERICA INC.;

    申请/专利号US201313908715

  • 发明设计人 RENQIANG MIN;YANJUN QI;

    申请日2013-06-03

  • 分类号G06N5/02;

  • 国家 US

  • 入库时间 2022-08-21 15:18:49

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