Combinatorics and connectionism

DOI10.1016/0012-365X(92)00048-VzbMath0798.68140OpenAlexW2075943509MaRDI QIDQ1313821

Norman L. Biggs

Publication date: 31 October 1994

Published in: Discrete Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0012-365x(92)00048-v

zbMATH Keywords

learningVapnik-Chervonenkis dimensionconnectionismhuman braincognitive functions

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Cognitive psychology (91E10)

Related Items (1)

A result of Vapnik with applications

Cites Work

• \(\epsilon\)-nets and simplex range queries
• Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
• Some special Vapnik-Chervonenkis classes
• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Decision theoretic generalizations of the PAC model for neural net and other learning applications
• Bounding sample size with the Vapnik-Chervonenkis dimension
• Randomised algorithms
• A combinatorial problem; stability and order for models and theories in infinitary languages
• On the density of families of sets
• Learnability and the Vapnik-Chervonenkis dimension
• A theory of the learnable
• Computational limitations on learning from examples
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
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