DISTRIBUTED SAMPLE SELECTION WITH SELF-LABELING

In some embodiments, techniques for self-labeling to extract a representative set of samples from a large-scale set of unlabeled documents (e.g., a set that represents a distribution of the large-scale set) are provided. The samples of the representative set may then be used to classify the document...

Full description

Saved in:
Bibliographic Details
Main Authors Zeng, Zhihong, Fouad, Meena Abdelmaseeh Adly, Chen, Zhi, Goli, Narasimha
Format Patent
LanguageEnglish
Published 05.10.2023
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
Online AccessGet full text

Cover

More Information
Summary:In some embodiments, techniques for self-labeling to extract a representative set of samples from a large-scale set of unlabeled documents (e.g., a set that represents a distribution of the large-scale set) are provided. The samples of the representative set may then be used to classify the documents of the large-scale set.
Bibliography:Application Number: US202318176922