Research map / Graph learning / Graph algorithms and clustering
Online Prediction of Switching Graph Labelings with Cluster Specialists
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SCS-F or SCS-B instead of the kernel Perceptron | Switching graph labeling prediction · Chicago Divvy Bicycle Sharing data | cumulative mistakes | Helped |
| Use SCS-F or SCS-B instead of the natural benchmarks | Switching graph labeling prediction · Chicago Divvy Bicycle Sharing data | cumulative mistakes | Helped |
| Replace the complete basis F_n with the binary-tree basis B_n (ablation) | SCS · Chicago Divvy Bicycle Sharing data | cumulative mistakes | Helped |
| Increase ensemble size from 1 to 65 random-spine instances (ablation) | SCS-F and SCS-B · Chicago Divvy Bicycle Sharing data | cumulative mistakes | Helped |
| Use the binary-tree basis B_n rather than F_n (ablation) | SCS · Graph labeling prediction | prediction time per trial: Θ(n^2) -> O(log n) | Helped |
| Tune the switching parameter α (ablation) | SCS-F · Chicago Divvy Bicycle Sharing data | cumulative mistakes | Mixed |
| Tune the switching parameter α (ablation) | SCS-B · Chicago Divvy Bicycle Sharing data | cumulative mistakes | No clear effect |
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