Research map / Learning theory and algorithms / Kernels and regression theory
Early stopping for kernel boosting algorithms: A general analysis with localized complexities
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the theoretically selected stopping rule T = (7 n)^0.67 instead of the oracle gold-standard stopping time | L²-Boost · First-order Sobolev kernel simulation | mean-squared error | Mixed |
| Use the theoretically selected stopping rule T = (7 n)^0.67 instead of the oracle gold-standard stopping time | LogitBoost · First-order Sobolev kernel simulation | mean-squared error | Mixed |
| Use exponent κ = 0.33 instead of the theoretically optimal κ = 0.67 (ablation) | L²-Boost · First-order Sobolev kernel simulation | mean-squared error | Hurt |
| Use exponent κ = 1 instead of the theoretically optimal κ = 0.67 (ablation) | L²-Boost · First-order Sobolev kernel simulation | mean-squared error | Hurt |
| Use exponent κ = 0.33 instead of the theoretically optimal κ = 0.67 (ablation) | LogitBoost · First-order Sobolev kernel simulation | mean-squared error | Hurt |
| Use exponent κ = 1 instead of the theoretically optimal κ = 0.67 (ablation) | LogitBoost · First-order Sobolev kernel simulation | mean-squared error | Hurt |
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