PAC-Bayesian compression bounds on the prediction error of learning algorithms for classification

DOI10.1007/s10994-005-0462-7zbMath1101.68561OpenAlexW2111049014MaRDI QIDQ5916202

Ralf Herbrich, John Shawe-Taylor, Thore Graepel

Publication date: 17 June 2005

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-005-0462-7

zbMATH Keywords

classificationerror boundssample compressionkernel classifiersPAC-Bayes

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30)

Related Items (8)

On the proliferation of support vectors in high dimensions* ⋮ The role of mutual information in variational classifiers ⋮ Learning the set covering machine by bound minimization and margin-sparsity trade-off ⋮ Unnamed Item ⋮ Active Nearest-Neighbor Learning in Metric Spaces ⋮ Learning a priori constrained weighted majority votes ⋮ On the fusion of threshold classifiers for categorization and dimensionality reduction ⋮ On the perceptron's compression

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