Who Cited It

Learnability and the Vapnik-Chervonenkis dimension

1989 · Journal of the ACM · 1,855 citations · 14 from inside this corpus

Anselm Blumer, Andrzej Ehrenfeucht low, David Haussler, Manfred K. Warmuth

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Learnability and the Vapnik-Chervonenkis dimension (1989)Learnability and the Vapnik-C…On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities (1971)On the Uniform Convergence of…Learning regular sets from queries and counterexamples (1987)Learning regular sets from qu…Queries and Concept Learning (1988)Queries and Concept LearningWhat Size Net Gives Valid Generalization? (1989)What Size Net Gives Valid Gen…Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm (1988)Learning Quickly When Irrelev…An overview of statistical learning theory (1999)An overview of statistical le…Semi-Supervised Learning (2006)Semi-Supervised LearningThe Strength of Weak Learnability (1990)The Strength of Weak Learnabi…Selection of relevant features and examples in machine learning (1997)Selection of relevant feature…Understanding Machine Learning: From Theory To Algorithms (2015)Understanding Machine Learnin…A Short Introduction to Boosting (1999)A Short Introduction to Boost…The strength of weak learnability (1990)The strength of weak learnabi…The Boosting Approach to Machine Learning: An Overview (2003)The Boosting Approach to Mach…Pruning algorithms-a survey (1993)Pruning algorithms-a surveyThe Lack of A Priori Distinctions Between Learning Algorithms (1996)The Lack of A Priori Distinct…Queries and Concept Learning (1988)Queries and Concept LearningWhat Size Net Gives Valid Generalization? (1989)What Size Net Gives Valid Gen…Learning in the Presence of Concept Drift and Hidden Contexts (1996)Learning in the Presence of C…Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm (1988)Learning Quickly When Irrelev…
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What this paper cites, inside the corpus

PaperYearCited
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities19712,435
Learning regular sets from queries and counterexamples19872,135
Queries and Concept Learning19881,636
What Size Net Gives Valid Generalization?19891,559
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm19881,382

What cites it, inside the corpus

PaperYearCited
An overview of statistical learning theory19996,311
Semi-Supervised Learning20064,334
The Strength of Weak Learnability19903,344
Selection of relevant features and examples in machine learning19973,309
Understanding Machine Learning: From Theory To Algorithms20153,081
A Short Introduction to Boosting19992,952
The strength of weak learnability19902,477
The Boosting Approach to Machine Learning: An Overview20032,023
Pruning algorithms-a survey19931,717
The Lack of A Priori Distinctions Between Learning Algorithms19961,661
Queries and Concept Learning19881,636
What Size Net Gives Valid Generalization?19891,559
Learning in the Presence of Concept Drift and Hidden Contexts19961,445
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm19881,382

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Topics

Machine Learning and AlgorithmsComputer Science
Computability, Logic, AI AlgorithmsComputer Science
Domain Adaptation and Few-Shot LearningComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports4 author record(s) attached.
  • supports79 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:52+00:00.

sha256 930f5bc64fea8a60…