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INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL

机译:即时加权学习机学习模型

摘要

An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.
机译:实例加权学习(IWL)机器学习模型。在一个示例实施例中,一种使用IWL机器学习模型来训练分类器的方法可以包括:确定在强化学习机器学习训练实例的时间序列中应该与每个机器学习训练实例相关联的质量值,将相应的确定的关联。质量值与每个机器学习训练实例,并使用每个机器学习训练实例训练分类器。每个机器学习训练实例都包括一个状态-动作对,并在训练期间根据其关联的质量值使用加权因子对加权值进行加权,该加权因子对不同的质量值进行不同的加权,从而使分类器从具有更高的机器学习训练实例中学习更多质量值比来自机器学习训练实例的质量值低。

著录项

  • 公开/公告号EP2936334A1

    专利类型

  • 公开/公告日2015-10-28

    原文格式PDF

  • 申请/专利权人 INSIDESALES.COM INC.;

    申请/专利号EP20130864581

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

    申请日2013-12-20

  • 分类号G06F15/18;

  • 国家 EP

  • 入库时间 2022-08-21 15:01:42

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