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Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension

https://doi.org/10.1007/bf00993163
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/0471200611openalex-snapshot
Elements of Information Theory
inferredcites →https://doi.org/10.1007/bf00322017openalex-snapshot
Donsker classes of sets
inferredcites →https://doi.org/10.1007/bf00994111openalex-snapshot
A Framework for Average Case Analysis of Conjunctive Learning Algorithms
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Rates of convergence in the central limit theorem for empirical processes
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On the density of families of sets
inferredcites →https://doi.org/10.1016/0097-3165(95)90052-7openalex-snapshot
Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension
inferredcites →https://doi.org/10.1016/0166-218x(93)90179-ropenalex-snapshot
Bounding sample size with the Vapnik-Chervonenkis dimension
inferredcites →https://doi.org/10.1016/b978-1-55860-146-8.50032-1openalex-snapshot
AGGREGATING STRATEGIES
inferredcites →https://doi.org/10.1016/b978-1-55860-213-7.50011-0openalex-snapshot
Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron With Noise
inferredcites →https://doi.org/10.1103/physrevlett.65.1683openalex-snapshot
Learning from examples in large neural networks
inferredcites →https://doi.org/10.1109/18.54897openalex-snapshot
Information-theoretic asymptotics of Bayes methods
inferredcites →https://doi.org/10.1109/ijcnn.1989.118274openalex-snapshot
Consistent inference of probabilities in layered networks: predictions and generalizations
inferredcites →https://doi.org/10.1109/sfcs.1988.21929openalex-snapshot
Learning probabilistic prediction functions
inferredcites →https://doi.org/10.1109/sfcs.1989.63487openalex-snapshot
The weighted majority algorithm
inferredcites →https://doi.org/10.1137/0221029openalex-snapshot
Probably Approximate Learning over Classes of Distributions
inferredcites →https://doi.org/10.1137/1116025openalex-snapshot
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
inferredcites →https://doi.org/10.1145/103418.103467openalex-snapshot
On-line learning of linear functions
inferredcites →https://doi.org/10.1145/1968.1972openalex-snapshot
A theory of the learnable
inferredcites →https://doi.org/10.1145/76359.76371openalex-snapshot
Learnability and the Vapnik-Chervonenkis dimension
inferredcites →https://doi.org/10.1162/neco.1989.1.1.151openalex-snapshot
What Size Net Gives Valid Generalization?
inferredcites →https://doi.org/10.2307/2988246openalex-snapshot
Estimation of Dependences Based on Empirical Data.
inferredcites →https://doi.org/10.5802/aif.938openalex-snapshot
Densité et dimension

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension
inferred← citeshttps://doi.org/10.1016/b978-0-12-801522-3.00012-4openalex-snapshot
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