Learning with confident examples: Rank pruning for robust classification with noisy labels
P N learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate ρ1 for positive examples and ρ0 for negative examples. We propose Rank Pruning (RP) to solve PN learning and the open problem of estimating the noise rates. Unlike prior...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2021
|
| Online Access: | https://hdl.handle.net/1721.1/137802 |