Research map / Optimization / Nonconvex and smooth optimization
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Pair the current instance with the previous instance instead of randomly selecting a pair | Pairwise AUC learning · diabetes, german, ijcnn1, letter, mnist, usps | AUC: diabetes: .830 -> .831; german: .794 -> .793; ijcnn1: .934 -> .934; letter: .811 -> .810; mnist: .932 -> .932; usps: .92 | Mixed |
| Use one previous instance rather than a large buffer | Pairwise AUC learning · AUC maximization, multiple LIBSVM datasets | CPU running time | Helped |
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