Learning with many experts: Model selection and sparsity

DOI10.1002/SAM.11206zbMath1281.62146arXiv1405.3292OpenAlexW3123150810MaRDI QIDQ2870765

Rafael Izbicki, Rafael B. Stern

Publication date: 21 January 2014

Published in: Statistical Analysis and Data Mining (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.3292

zbMATH Keywords

classificationEM algorithmnoisy datarisk minimizationsparsitycrowdsourcingmultiple annotators

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Teaching Decision Theory Proof Strategies Using a Crowdsourcing Problem ⋮ Prior Shift Using the Ratio Estimator ⋮ A Comparison of Two Methods for Obtaining a Collective Posterior Distribution ⋮ Unnamed Item

Uses Software

• glmnet
• ElemStatLearn

Cites Work

• Unnamed Item
• The EM Algorithm and Extensions, 2E
• Non-uniqueness in probabilistic numerical identification of bacteria

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