Research map / Learning theory and algorithms / Prediction and decision losses
Logistic Boosting Regression for Label Distribution Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use an adaptive one-vs-one vector-tree base learner instead of weighted regression trees | LDLogitBoost · s-BU 3DFE | K-L: .0515±.0027 -> .0491±.0023 | Helped |
| Use an adaptive one-vs-one vector-tree base learner instead of weighted regression trees | LDLogitBoost · COPM | K-L: .0900±.0038 -> .0855±.0037 | Helped |
| Predict an age distribution using apparent-age mean and standard deviation rather than a single age | LogitBoost · ChaLearn Age Estimation | MAE: 9.0458 -> 7.3449 | Helped |
| Predict an age distribution using apparent-age mean and standard deviation rather than a single age | AOSO-LogitBoost · ChaLearn Age Estimation | MAE: 8.0361 -> 7.2949 | Helped |
| Use a vector-tree base learner rather than a weighted regression tree | LDLogitBoost · s-BU 3DFE | Euclidean: .1297±.0039 -> .1263±.0033 | Helped |
| Use a vector-tree base learner rather than a weighted regression tree | LDLogitBoost · COPM | Euclidean: .1585±.0032 -> .1547±.0030 | Helped |
Closest papers by meaning
- Approximately Correct Label Distribution Learning (ICML 2025 · similarity 0.88)
- Concentration Distribution Learning from Label Distributions (ICML 2025 · similarity 0.87)
- Generative Calibration of Inaccurate Annotation for Label Distribution Learning (AAAI 2024 · similarity 0.85)
- Divide and Conquer: Learning Label Distribution with Subtasks (ICML 2025 · similarity 0.84)
- Adaptive-Grained Label Distribution Learning (AAAI 2025 · similarity 0.84)
- Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood (AAAI 2024 · similarity 0.82)
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