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In various situations one is given only the predictions of multiple classifiers over a large unlabeled test data.
Maximum likelihood estimation of observer error-rates using the em algorith
A. P Dawid and A. M Skene · 1979
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Uniform convergence in probability and stochastic equicontinuity
W. K. Newey · 1991
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Estimating a mixture of two product distributions
Y. Freund and Y. Mansour · 1999
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Ensemble methods in machine learning
T.G. Dietterich · 2000
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Learning mixtures of product distributions over discrete domains
J. Feldman, R. O’Donnell, and R.A. Servedio · 2008
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Get another label? improving data quality and data mining using multiple, noisy labelers
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Cheap and fast but is it good?
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P. Welinder, S. Branson, S. Belongie, and P. Perona · 2010
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UCI machine learning repository, 2013
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Learning mixtures of discrete product distributions using spectral decompositions
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Square: A benchmark for research on computing crowd concensus
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Ranking and combining multiple predictors without labeled data
F. Parisi, F. Strino, B. Nadler, and Y. Kluger · 2014
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Estimating accuracy from unlabeled data
E.A. Platanios, A. Blum, and T. Mitchell · 2014
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Spectral methods meet em: A provably optimal algorithm for crowdsourcin
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Budget-optimal crowdsourcing using low-rank matrix approximations
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Tensor decompositions for learning latent variable models
A. Anandkumar, R. Ge, D. Hsu, S.M. Kakade, and M. Telgarsky
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A method of moments for mixture models and hidden markov models
A. Anandkumar, D. Hsu, and S.M. Kakade
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Y. Zhang, X. Chen, D. Zhou, and M.I. Jordan · 2014
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