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Tracking Drifting Concepts By Minimizing Disagreements
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Convergence of Stochastic Processes
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A general lower bound on the number of examples needed for learning
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Decision theoretic generalizations of the PAC model for neural net and other learning applications
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Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures)
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THE LEARNABILITY OF FORMAL CONCEPTS
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Tracking Drifting Concepts Using Random Examples
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A Markovian extension of Valiant's learning model
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The weighted majority algorithm
inferredcites →https://doi.org/10.1137/1116025openalex-snapshot
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
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Computational limitations on learning from examples
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Learnability and the Vapnik-Chervonenkis dimension
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Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications
inferredcites →https://doi.org/10.5555/114836.114839openalex-snapshot
Tracking drifting concepts using random examples
inferredcites →https://doi.org/10.5555/92571.92649openalex-snapshot
The learnability of formal concepts
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A general lower bound on the number of examples needed for learning
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Inductive principles of the search for empirical dependences (methods based on weak convergence of probability measures)
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