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ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION

JAMEEL, SYED MUSLIM (2021) ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION. PhD thesis, Universiti Teknologi PETRONAS.

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Abstract

The analysis from the data streams is an essential requirement in the current era of digitalization. However, the critical features of many real-world data streams (imagery streams) such as high-dimensionality, large size, and nonstationary nature lead to concept drift, cause the characteristics of the data streams can change arbitrarily over time. The presence of concept drift renders many classical machine learning approaches unsuitable, hence research community must address this critical issue and contribute towards new adaptive approaches in their place.

Item Type: Thesis (PhD)
Academic Subject : Academic Department - Information Communication Technology
Subject: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 08 Sep 2021 12:07
Last Modified: 08 Sep 2021 12:07
URI: http://utpedia.utp.edu.my/id/eprint/20723

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