Variance Reduction Through Bootstrap Ensemble Aggregation

via “variance-reduction through bootstrap ensemble aggregation”

* 🏆 1998: [Gradient-based learning applied to document recognition (CNN/GTN)](https://ieeexplore.ieee.org/abstract/document/726791)

Unique: Introduces bootstrap resampling (sampling with replacement) as a principled mechanism to create diverse training sets for ensemble members, enabling variance reduction without requiring base learner modification or access to additional data — a novel approach in 1996 that differs from prior ensemble methods by leveraging statistical resampling theory rather than algorithmic manipulation

Simpler and more general than boosting (no sequential weighting or adaptive resampling required) and applicable to any base learner, but less effective at bias reduction than boosting and only beneficial for unstable predictors unlike boosting's broader applicability