Machine Learning Algorithms Applied to Corrupted Data Recovery: A Comprehensive Survey

This preprint presents a narrative survey and conceptual synthesis of machine learning approaches applied to corrupted data recovery, including supervised, unsupervised, generative, transformer-based, and reinforcement learning methods.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-23
DOI
https://doi.org/10.5281/zenodo.20353908
Primary Topic
Data Stream Mining Techniques
Type
preprint
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preprint

Machine Learning Algorithms Applied to Corrupted Data Recovery: A Comprehensive Survey

Erika Conta
Zenodo (CERN European Organization for Nuclear Research)
Data Stream Mining Techniques
preprint

Machine Learning Algorithms Applied to Corrupted Data Recovery: A Comprehensive Survey

Erika Conta
preprint en

Abstract

This preprint presents a narrative survey and conceptual synthesis of machine learning approaches applied to corrupted data recovery, including supervised, unsupervised, generative, transformer-based, and reinforcement learning methods.

Zenodo (CERN European Organization for Nuclear Research)
SMART Reading (US)
Data Stream Mining Techniques
DOIOpen Access PDFOpenAlex
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