Transfer learning method and system for large-scale data calibration

Transfer learning method and system for large-scale data calibration

  • CN 106,599,922 B
  • Filed: 12/16/2016
  • Issued: 08/24/2021
  • Est. Priority Date: N/A
  • Status: Active Grant
First Claim
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1. A migration learning method for large-scale data calibration comprises the following steps:

  • step a) calibrating target domain data to be calibrated respectively by utilizing at least two classifiers trained based on calibrated source domain data, and adopting most voting criteria to obtain consistent target domain data of calibration results to form a candidate set and form the rest of the target domain data for the calibration results of the at least two classifiers;

    step b) grouping the data according to the calibration of the source domain data and the target domain data of the candidate set respectively, transforming the source domain data group and the target domain data group with the same calibration to the same space to enable the transformed source domain data group and the transformed target domain data group to meet the same distribution, and merging the transformed source domain data group and target domain data group into a new source domain and a new candidate set respectively;

    step c) calibrating the target domain data in the new candidate set based on the classifier trained on the new source domain, and updating the calibration of each data in the untransformed candidate set by using the calibration result of each data in the new candidate set;

    step d) training a classifier based on the updated and calibrated candidate set, and completing calibration of target data in the rest part by using the classifier;

    wherein the source domain data is behavior data and a marker corresponding to a part of a human body;

    the target domain data is behavior data corresponding to another part of the human body.

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