Imprecise Deep Forest for Partial Label Learning
In partial label (PL) learning, each instance corresponds to a set of candidate labels, among which only one is valid. The objective of PL learning is to obtain a multi-class classifier from the training instances. Because the true label of a PL training instance is hidden in the candidate label set...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9284528/ |