首页> 中文期刊> 《应用数学与计算数学学报》 >A Non-intrusive Correction Algorithm for Classification Problems with Corrupted Data

A Non-intrusive Correction Algorithm for Classification Problems with Corrupted Data

     

摘要

A novel correction algorithm is proposed for multi-class classification problems with cor-rupted training data.The algorithm is non-intrusive,in the sense that it post-processes a trained classification model by adding a correction procedure to the model prediction.The correction procedure can be coupled with any approximators,such as logistic regression,neural networks of various architectures,etc.When the training dataset is sufficiently large,we theoretically prove(in the limiting case)and numerically show that the corrected mod-els deliver correct classification results as if there is no corruption in the training data.For datasets of finite size,the corrected models produce significantly better recovery results,compared to the models without the correction algorithm.All of the theoretical findings in the paper are verified by our numerical examples.

著录项

  • 来源
    《应用数学与计算数学学报》 |2021年第2期|337-356|共20页
  • 作者

  • 作者单位

    俄亥俄州立大学;

  • 原文格式 PDF
  • 正文语种 chi
  • 中图分类
  • 关键词

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