Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection
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Iñaki Inza | José Antonio Lozano | Jerónimo Hernández-González | J. A. Lozano | Iñaki Inza | J. Hernández-González
[1] Zhi-Hua Zhou,et al. Multi-Label Learning with Weak Label , 2010, AAAI.
[2] Iñaki Inza,et al. Learning from Crowds in Multi-dimensional Classification Domains , 2013, CAEPIA.
[3] Janyce Wiebe,et al. Development and Use of a Gold-Standard Data Set for Subjectivity Classifications , 1999, ACL.
[4] Pietro Perona,et al. The Multidimensional Wisdom of Crowds , 2010, NIPS.
[5] S. Sathiya Keerthi,et al. Regularized Structured Output Learning with Partial Labels , 2012, SDM.
[6] Thierry Denoeux,et al. Evidential Multi-Label Classification Approach to Learning from Data with Imprecise Labels , 2010, IPMU.
[7] Brendan T. O'Connor,et al. Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.
[8] Pietro Perona,et al. Inferring Ground Truth from Subjective Labelling of Venus Images , 1994, NIPS.
[9] Xindong Wu,et al. Eliminating Class Noise in Large Datasets , 2003, ICML.
[10] Arthur M. Breipohl,et al. An Error Correcting Procedure for Learning with an Imperfect Teacher , 1971, IEEE Trans. Syst. Man Cybern..
[11] David Heckerman,et al. A Tutorial on Learning with Bayesian Networks , 1998, Learning in Graphical Models.
[12] Zoran Obradovic,et al. Learning by aggregating experts and filtering novices: a solution to crowdsourcing problems in bioinformatics , 2013, BMC Bioinformatics.
[13] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[14] Javier R. Movellan,et al. Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise , 2009, NIPS.
[15] Min-Ling Zhang,et al. A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.
[16] S. García,et al. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons , 2008 .
[17] Concha Bielza,et al. Bayesian network modeling of the consensus between experts: An application to neuron classification , 2014 .
[18] Rong Jin,et al. Learning with Multiple Labels , 2002, NIPS.
[19] A. P. Dawid,et al. Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm , 1979 .
[20] Gerardo Hermosillo,et al. Learning From Crowds , 2010, J. Mach. Learn. Res..
[21] Friedhelm Schwenker,et al. Partially supervised learning for pattern recognition , 2014, Pattern Recognit. Lett..
[22] Gábor Lugosi,et al. Learning with an unreliable teacher , 1992, Pattern Recognit..
[23] José Antonio Lozano,et al. Using Multidimensional Bayesian Network Classifiers to Assist the Treatment of Multiple Sclerosis , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[24] Ben Taskar,et al. Learning from Partial Labels , 2011, J. Mach. Learn. Res..
[25] Ohad Shamir,et al. Good learners for evil teachers , 2009, ICML '09.
[26] Rong Jin,et al. Multi-label learning with incomplete class assignments , 2011, CVPR 2011.
[27] Concha Bielza,et al. Multi-dimensional classification with Bayesian networks , 2011, Int. J. Approx. Reason..
[28] Panagiotis G. Ipeirotis,et al. Get another label? improving data quality and data mining using multiple, noisy labelers , 2008, KDD.
[29] Dana M. Caudle,et al. To tag or not to tag? , 2009, Libr. Hi Tech.
[30] Avrim Blum,et al. Veritas: Combining Expert Opinions without Labeled Data , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.
[31] Nir Friedman,et al. Learning Belief Networks in the Presence of Missing Values and Hidden Variables , 1997, ICML.
[32] Milos Hauskrecht,et al. Learning classification models from multiple experts , 2013, J. Biomed. Informatics.
[33] Ohad Shamir,et al. Vox Populi: Collecting High-Quality Labels from a Crowd , 2009, COLT.
[34] Carla E. Brodley,et al. Identifying Mislabeled Training Data , 1999, J. Artif. Intell. Res..
[35] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .