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public:multi-label_classification
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public:multi-label_classification [2019/03/22 19:14] htlin [thesis] |
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| ====== Introduction ====== | ====== Introduction ====== | ||
| Multiclass classification is an important problem in machine learning. It can be used in a variety of applications, such as organizing documents to different categories automatically. Multi-label classification is an extension of multi-class classification --- the former allows a set of labels to be associated with an instance while the latter allows only one. For instance, a document may belong to both the "politics" and "health" class if it is about the National Health Insurance. Many other similar applications arise in domains like text mining, vision, or bio-informatics. | Multiclass classification is an important problem in machine learning. It can be used in a variety of applications, such as organizing documents to different categories automatically. Multi-label classification is an extension of multi-class classification --- the former allows a set of labels to be associated with an instance while the latter allows only one. For instance, a document may belong to both the "politics" and "health" class if it is about the National Health Insurance. Many other similar applications arise in domains like text mining, vision, or bio-informatics. | ||
| - | ====== Our Related Work ====== | + | ====== Our Related Works ====== |
| ===== paper ===== | ===== paper ===== | ||
| * [HC2019] Hong-Min Chu, Kuan-Hao Huang, and Hsuan-Tien Lin. Dynamic principal projection for cost-sensitive online multi-label classification. Machine Learning, 2019. Accepted, to be presented in the journal track of ECML '19. | * [HC2019] Hong-Min Chu, Kuan-Hao Huang, and Hsuan-Tien Lin. Dynamic principal projection for cost-sensitive online multi-label classification. Machine Learning, 2019. Accepted, to be presented in the journal track of ECML '19. | ||
public/multi-label_classification.txt · Last modified: 2021/08/30 07:27 by htlin
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