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Neural Networks with Quadratic VC Dimension
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Theoretical Advances in Neural Computation and Learning
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Perspectives of Current Research about the Complexity of Learning on Neural Nets
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Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers
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Scale-sensitive dimensions, uniform convergence, and learnability
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Bounds for the Computational Power and Learning Complexity of Analog Neural Nets
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Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers
inferredcites →https://doi.org/10.1145/225058.225118openalex-snapshot
Polynomial bounds for VC dimension of sigmoidal neural networks
inferredcites →https://doi.org/10.1145/76359.76371openalex-snapshot
Learnability and the Vapnik-Chervonenkis dimension
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What Size Net Gives Valid Generalization?
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Sigmoids Distinguish More Efficiently Than Heavisides
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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