Interactive Causal Correlation Space Reshape for Multi-Label Classification
Most existing multi-label classification models focus on distance metrics and feature spare strategies to extract specific features of labels. Those models use the cosine similarity to construct the label correlation matrix to constraint solution space, and then mine the latent semantic information...
Κύριοι συγγραφείς: | Chao Zhang, Yusheng Cheng, Yibin Wang, Yuting Xu |
---|---|
Μορφή: | Άρθρο |
Γλώσσα: | English |
Έκδοση: |
Universidad Internacional de La Rioja (UNIR)
2022-09-01
|
Σειρά: | International Journal of Interactive Multimedia and Artificial Intelligence |
Θέματα: | |
Διαθέσιμο Online: | https://www.ijimai.org/journal/bibcite/reference/3159 |
Παρόμοια τεκμήρια
Παρόμοια τεκμήρια
-
Application of Label Correlation in Multi-Label Classification: A Survey
ανά: Shan Huang, κ.ά.
Έκδοση: (2024-10-01) -
Robust Multi-Label Classification with Enhanced Global and Local Label Correlation
ανά: Tianna Zhao, κ.ά.
Έκδοση: (2022-05-01) -
Soft-label recover based label-specific features learning
ανά: Jiansheng Jiang, κ.ά.
Έκδοση: (2024-10-01) -
ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method
ανά: Xiangeng Wang, κ.ά.
Έκδοση: (2019-09-01) -
Joint Label-Density-Margin Space and Extreme Elastic Net for Label-Specific Features
ανά: Gensheng Pei, κ.ά.
Έκδοση: (2019-01-01)