Ensemble classification and its application to visual tracking
In machine learning and statistics, ensemble methods employ multiple models to obtain better performance than that could be obtained from any of the constituent (base) models [1]. Many studies have been published, both theoretical and empirical, which demonstrate the advantages of ensemble methods f...
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sg-ntu-dr.10356-690732023-07-04T16:32:23Z Ensemble classification and its application to visual tracking Zhang, Le Ponnuthurai N. Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering In machine learning and statistics, ensemble methods employ multiple models to obtain better performance than that could be obtained from any of the constituent (base) models [1]. Many studies have been published, both theoretical and empirical, which demonstrate the advantages of ensemble methods for classification. DOCTOR OF PHILOSOPHY (EEE) 2016-10-13T04:51:46Z 2016-10-13T04:51:46Z 2016 Thesis Zhang, L. (2016). Ensemble classification and its application to visual tracking. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/69073 10.32657/10356/69073 en 145 p. application/pdf |
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Nanyang Technological University |
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NTU Library |
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Asia |
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Singapore Singapore |
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NTU Library |
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English |
| topic |
DRNTU::Engineering::Computer science and engineering |
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DRNTU::Engineering::Computer science and engineering Zhang, Le Ensemble classification and its application to visual tracking |
| description |
In machine learning and statistics, ensemble methods employ multiple models to obtain better performance than that could be obtained from any of the constituent (base) models [1]. Many studies have been published, both theoretical and empirical, which demonstrate the advantages of ensemble methods for classification. |
| author2 |
Ponnuthurai N. Suganthan |
| author_facet |
Ponnuthurai N. Suganthan Zhang, Le |
| format |
Theses and Dissertations |
| author |
Zhang, Le |
| author_sort |
Zhang, Le |
| title |
Ensemble classification and its application to visual tracking |
| title_short |
Ensemble classification and its application to visual tracking |
| title_full |
Ensemble classification and its application to visual tracking |
| title_fullStr |
Ensemble classification and its application to visual tracking |
| title_full_unstemmed |
Ensemble classification and its application to visual tracking |
| title_sort |
ensemble classification and its application to visual tracking |
| publishDate |
2016 |
| url |
https://hdl.handle.net/10356/69073 |
| _version_ |
1772827856788783104 |