Research map / Learning theory and algorithms / Prediction and decision losses
Optimal Classification with Multivariate Losses
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed optimal top-k prediction (sort by conditional probability, Algorithm 1/2) instead of thresholding at 0.5 | F1 and Jaccard losses on multi-dataset benchmark · Benchmark datasets (Table 2) | test loss (lower better) | Mixed |
| Use proposed optimal top-k prediction instead of structured SVM | AM loss · Benchmark datasets (Table 3) | test AM loss (lower better) | Mixed |
| Use structured SVM instead of proposed method for G-TP/PR loss | G-TP/PR loss · Benchmark datasets (Table 3) | test G-TP/PR loss (lower better) | Mixed |
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