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CONSTRAINED SAMPLE SELECTION FOR TRAINING MODELS

机译:训练模型的约束样本选择

摘要

Methods, apparatus, and machine-readable mediums are described for selecting a training set from a larger data set. Samples are divided into a training set and a validation set. Each set meets one or more conditions. For each class to be modeled, multiple training sets are created. Models are trained on each of the multiple training sets. A size of samples for each class is determined based upon the trained models. A training data set that includes a number of samples based upon the determined size of samples is created.
机译:描述了用于从较大的数据集中选择训练集的方法,装置和机器可读介质。样本分为训练集和验证集。每一套都符合一个或多个条件。对于每个要建模的课程,将创建多个训练集。在多个训练集的每个训练集上训练模型。根据训练后的模型确定每个类别的样本大小。基于确定的样本大小,创建包括多个样本的训练数据集。

著录项

  • 公开/公告号US2019065989A1

    专利类型

  • 公开/公告日2019-02-28

    原文格式PDF

  • 申请/专利权人 INTEL CORPORATION;

    申请/专利号US201715691632

  • 发明设计人 LUIS SERGIO KIDA;

    申请日2017-08-30

  • 分类号G06N99;G06F17/18;

  • 国家 US

  • 入库时间 2022-08-21 12:05:14

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