The Strength of Weak Learnability

Schapire, R. E. (1990). The Strength of Weak Learnability. Machine Learning, 5(2), 197-227.

Type

Journal article

Intellectual base

Primary research

Year

1990

DOI

10.1007/bf00116037

Link

https://doi.org/10.1007/BF00116037

Cited from

pharmacology_toxicology

Cited by

3 citations across 3 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Potentiation
  • The construct manages the complexity of dynamic response by providing a named category for history-dependent sensitization—structurally parallel to tolerance but mechanistically and directionally distinct—that enables separate characterization of sensitizing mechanism, temporal course, dose-response dynamics, and management strategy, in much the way Schapire (1990) demonstrated that sequential weak-learner amplification (boosting) can be reasoned about as a binary directional decision before mechanistic elaboration.

    This sourceFoundational boosting result: sequential weak-learner amplification yields disproportionate aggregate response, framed via a binary directional decision (boost vs. not) before mechanistic elaboration of the amplification scheme.

Domain-specific

• AdaBoost
  • The theoretical weak-learning condition means performance with an edge under the distributions presented to it, not small model size, shallow trees, or poor quality in ordinary language.
• Boosting
  • Schapire proved the equivalence of weak and strong learnability and gave a boosting construction.

Verification

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