What is recorded here?
Proved, in the paper's distribution-free PAC framework, that weak and strong learnability are equivalent and gave a construction that raises a weak learner to arbitrarily high accuracy.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is Schapire's sole-author 1990 Machine Learning article. The theorem uses the paper's PAC assumptions; it does not absorb Kearns and Valiant's prior formulation, Freund–Schapire AdaBoost or later practical boosting analyses.
Read the original work
- Princeton record for The Strength of Weak Learnability
- Springer DOI for The Strength of Weak Learnability
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Robert E. Schapire
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
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Documented countries / regions: United States
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English catalogue narrative
Schapire: the strength of weak learnability
Proved, in the paper's distribution-free PAC framework, that weak and strong learnability are equivalent and gave a construction that raises a weak learner to arbitrarily high accuracy.
Scientific scope
The record is Schapire's sole-author 1990 Machine Learning article. The theorem uses the paper's PAC assumptions; it does not absorb Kearns and Valiant's prior formulation, Freund–Schapire AdaBoost or later practical boosting analyses.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Robert E. Schapire
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.