ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION
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...
| Main Author: | JAMEEL, SYED MUSLIM |
|---|---|
| Format: | Thesis |
| Language: | English |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-utpedia.20723 / |
| Published: |
2021
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| Subjects: | |
| Online Access: |
http://utpedia.utp.edu.my/20723/1/Syed%20Muslim%20Jameel_16000370.pdf http://utpedia.utp.edu.my/20723/ |
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utp-utpedia.207232021-09-08T12:07:55Z http://utpedia.utp.edu.my/20723/ ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION JAMEEL, SYED MUSLIM QA Mathematics QA75 Electronic computers. Computer science 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. 2021-03 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20723/1/Syed%20Muslim%20Jameel_16000370.pdf JAMEEL, SYED MUSLIM (2021) ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION. PhD thesis, Universiti Teknologi PETRONAS. |
| institution |
Universiti Teknologi Petronas |
| collection |
UTPedia |
| language |
English |
| topic |
QA Mathematics QA75 Electronic computers. Computer science |
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QA Mathematics QA75 Electronic computers. Computer science JAMEEL, SYED MUSLIM ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION |
| description |
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. |
| format |
Thesis |
| author |
JAMEEL, SYED MUSLIM |
| author_sort |
JAMEEL, SYED MUSLIM |
| title |
ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION |
| title_short |
ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION |
| title_full |
ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION |
| title_fullStr |
ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION |
| title_full_unstemmed |
ADAPTIVE CNN ENSEMBLE TO HANDLE CONCEPT DRIFT IN ONLINE IMAGE CLASSIFICATION |
| title_sort |
adaptive cnn ensemble to handle concept drift in online image classification |
| publishDate |
2021 |
| url |
http://utpedia.utp.edu.my/20723/1/Syed%20Muslim%20Jameel_16000370.pdf http://utpedia.utp.edu.my/20723/ |
| _version_ |
1741195657771745280 |
| score |
11.62408 |