On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis

DOI10.1016/J.NEUNET.2012.03.006zbMath1245.68164OpenAlexW2060063574WikidataQ47905229 ScholiaQ47905229MaRDI QIDQ448319

Keisuke Yamazaki

Publication date: 30 August 2012

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2012.03.006

zbMATH Keywords

feature selectionBayes learningsequential data model

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Cites Work

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• Lectures on algebraic statistics
• Estimating the dimension of a model
• An introduction to MCMC for machine learning
• Singularities in mixture models and upper bounds of stochastic complexity.
• Asymptotic analysis of Bayesian generalization error with Newton diagram
• Stochastic complexities of reduced rank regression in Bayesian estimation
• Algebraic Analysis for Nonidentifiable Learning Machines
• Algebraic Geometry and Statistical Learning Theory
• A new look at the statistical model identification

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