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Multiple output relaxation machine learning model

机译:多输出松弛机器学习模型

摘要

A multiple output relaxation (MOR) machine learning model. In one example embodiment, a method for employing an MOR machine learning model to predict multiple interdependent output components of a multiple output dependency (MOD) output decision may include training a classifier for each of multiple interdependent output components of an MOD output decision to predict the component based on an input and based on all of the other components. The method may also include initializing each possible value for each of the components to a predetermined output value. The method may further include running relaxation iterations on each of the classifiers to update the output value of each possible value for each of the components until a relaxation state reaches an equilibrium or a maximum number of relaxation iterations is reached. The method may also include retrieving an optimal component from each of the classifiers.
机译:多输出松弛(MOR)机器学习模型。在一个示例实施例中,一种用于采用MOR机器学习模型来预测多输出依赖(MOD)输出决策的多个相互依赖的输出分量的方法,可以包括针对MOD输出决策的多个相互依赖的输出分量中的每一个训练分类器以预测所述输出。组件基于输入和所有其他组件。该方法还可以包括将每个分量的每个可能值初始化为预定输出值。该方法可以进一步包括在每个分类器上运行弛豫迭代以更新每个分量的每个可能值的输出值,直到弛豫状态达到平衡或达到最大数目的弛豫迭代为止。该方法还可以包括从每个分类器检索最优分量。

著录项

  • 公开/公告号AU2013305924A1

    专利类型

  • 公开/公告日2015-03-12

    原文格式PDF

  • 申请/专利权人 INSIDESALES.COM INC.;

    申请/专利号AU2013305924A1

  • 发明设计人 ZENG XINCHUAN;MARTINEZ TONY RAMON;

    申请日2013-08-20

  • 分类号G06F15/18;

  • 国家 AU

  • 入库时间 2022-08-21 15:10:04

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