// ◇ Classical Machine Learning
AdaBoost
Chains weak learners, each focusing on the examples the previous ones got wrong.
1997 Foundational core
IN PLAIN WORDS
Train a weak classifier, up-weight the examples it misclassifies, train the next one, repeat, then take an accuracy-weighted vote. It was the first practical boosting algorithm; gradient boosting (GBDT) later generalized it to any loss.
Used in: Viola–Jones face detection (2001), tabular classification baselines
01Original paper
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund, Robert E. Schapire · Journal of Computer and System Sciences · 1997
DOI ↗ ✓ Verified · Crossref API doi:10.1006/jcss.1997.1504
02Where it sits
Learn the left column first; the right column builds on this one.
Related:Gradient Boosting (GBDT)Random Forest
Left: prerequisites · right: builds on this · dashed: related. Full map →
03Learning resources
Official docs, explainers, courses and reference code. All links checked.
04Where the course teaches it
For AI / data
- Verification: verified, checked 2026-10-22.
- JSON: /api/learn/algorithms.json (id:
adaboost)