Methods for Generating Drift in Text Streams

Systems and individuals produce data continuously. On the Internet, people share their knowledge, sentiments, and opinions, provide reviews about services and products, and so on. Automatically learning from these textual data can provide insights to organizations and institutions, thus preventing f...

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Veröffentlicht in:	arXiv.org 2024-03
Hauptverfasser:	Cristiano Mesquita Garcia, Koerich, Alessandro Lameiras, Alceu de Souza Britto Jr, Barddal, Jean Paul
Format:	Artikel
Sprache:	eng
Schlagworte:	Datasets Drift Machine learning Performance degradation Production methods
Online-Zugang:	Volltext
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