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System and method for efficient generation of machine-learning models

机译:高效生成机器学习模型的系统和方法

摘要

A system for determining data requirements to generate machine-learning models. The system may include one or more processors and one or more storage devices storing instructions. When executed, the instructions may configure the one or more processors to perform operations including: receiving a sample dataset, generating a plurality of data categories based on the sample dataset; generating a plurality of primary models of different model types using data from the corresponding one of the data categories as training data; generating a sequence of secondary models by training the corresponding one of the primary models with progressively less training data; identifying minimum viable models in the sequences of secondary models; determining a number of samples required for the minimum viable models; and generating entries in the database associating: model types; corresponding data categories; and corresponding numbers of samples in the training data used for the minimum viable models.
机译:用于确定数据要求以生成机器学习模型的系统。该系统可以包括一个或多个处理器和存储指令的一个或多个存储设备。当执行时,指令可以配置一个或多个处理器以执行包括:接收示例数据集,基于示例数据集生成多个数据类别;使用来自相应的数据类别的数据作为训练数据生成多个不同模型类型的主要模型;通过逐步越来越少的训练数据训练相应的一个主要模型来生成一系列辅助模型;识别辅助模型序列中的最小可行模型;确定最小可行模型所需的许多样品;并在数据库关联中生成条目:模型类型;相应的数据类别;在用于最小可行模型的训练数据中,相应数量的样本。

著录项

  • 公开/公告号US11030484B2

    专利类型

  • 公开/公告日2021-06-08

    原文格式PDF

  • 申请/专利权人 CAPITAL ONE SERVICES LLC;

    申请/专利号US201916361596

  • 申请日2019-03-22

  • 分类号G06K9/62;G06F9/54;G06N20;G06F16/9035;

  • 国家 US

  • 入库时间 2022-08-24 19:05:23

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