Approximating the crowd

DOI10.1007/s10618-014-0354-1zbMath1342.68267OpenAlexW2068882086MaRDI QIDQ736507

Haym Hirsh, Şeyda Ertekin, Cynthia Rudin

Publication date: 4 August 2016

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-014-0354-1

zbMATH Keywords

crowdsourcingwisdom of crowdsaggregating opinionsapproximating the crowdlabeler quality estimation

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Approximation algorithms (68W25) Online algorithms; streaming algorithms (68W27)

Related Items (max. 100)

Unnamed Item ⋮ Bandit-Based Task Assignment for Heterogeneous Crowdsourcing

Cites Work

• Repeated labeling using multiple noisy labelers
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