Ensemble Learning from Crowds
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Victor S. Sheng | Jing Zhang | Ming Wu | V. Sheng | Jing Zhang | Ming Wu
[1] Joan Bruna,et al. Training Convolutional Networks with Noisy Labels , 2014, ICLR 2014.
[2] A. P. Dawid,et al. Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm , 1979 .
[3] M. Verleysen,et al. Classification in the Presence of Label Noise: A Survey , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[4] Xindong Wu,et al. Learning from crowdsourced labeled data: a survey , 2016, Artificial Intelligence Review.
[5] Hiroshi Kajino,et al. Convex Formulations of Learning from Crowds , 2012 .
[6] Mark W. Schmidt,et al. Modeling annotator expertise: Learning when everybody knows a bit of something , 2010, AISTATS.
[7] Andrés R. Masegosa,et al. Bagging Decision Trees on Data Sets with Classification Noise , 2010, FoIKS.
[8] Thomas G. Dietterich. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.
[9] David J. Hand,et al. An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise , 2003, Multiple Classifier Systems.
[10] Carla E. Brodley,et al. Identifying Mislabeled Training Data , 1999, J. Artif. Intell. Res..
[11] Pietro Perona,et al. The Multidimensional Wisdom of Crowds , 2010, NIPS.
[12] Xindong Wu,et al. Eliminating Class Noise in Large Datasets , 2003, ICML.
[13] Nihar B. Shah,et al. On the Impossibility of Convex Inference in Human Computation , 2014, AAAI.
[14] Massimiliano Pontil,et al. Regularized multi--task learning , 2004, KDD.
[15] Chao Huang,et al. Where are you from: Home location profiling of crowd sensors from noisy and sparse crowdsourcing data , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.
[16] Francisco Herrera,et al. On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification , 2014, Neurocomputing.
[17] Gianluca Demartini,et al. ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking , 2012, WWW.
[18] Fenglong Ma,et al. Crowdsourcing High Quality Labels with a Tight Budget , 2016, WSDM.
[19] Wilfred Ng,et al. Truth Discovery in Data Streams: A Single-Pass Probabilistic Approach , 2014, CIKM.
[20] Zhuowen Tu,et al. Learning to Predict from Crowdsourced Data , 2014, UAI.
[21] Claudio Gentile,et al. Selective sampling and active learning from single and multiple teachers , 2012, J. Mach. Learn. Res..
[22] Wenxin Jiang,et al. Some Theoretical Aspects of Boosting in the Presence of Noisy Data , 2001, ICML.
[23] Chao Huang,et al. Topic-Aware Social Sensing with Arbitrary Source Dependency Graphs , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
[24] Bo Zhao,et al. Conflicts to Harmony: A Framework for Resolving Conflicts in Heterogeneous Data by Truth Discovery , 2016, IEEE Transactions on Knowledge and Data Engineering.
[25] Nagarajan Natarajan,et al. Learning with Noisy Labels , 2013, NIPS.
[26] Hongwei Li,et al. Error Rate Analysis of Labeling by Crowdsourcing , 2013 .
[27] Victor S. Sheng. Simple Multiple Noisy Label Utilization Strategies , 2011, 2011 IEEE 11th International Conference on Data Mining.
[28] Jaime G. Carbonell,et al. Proactive learning: cost-sensitive active learning with multiple imperfect oracles , 2008, CIKM '08.
[29] Panagiotis G. Ipeirotis,et al. Get another label? improving data quality and data mining using multiple, noisy labelers , 2008, KDD.
[30] Jennifer G. Dy,et al. Active Learning from Crowds , 2011, ICML.
[31] John C. Platt,et al. Learning from the Wisdom of Crowds by Minimax Entropy , 2012, NIPS.
[32] Matthew Lease,et al. SQUARE: A Benchmark for Research on Computing Crowd Consensus , 2013, HCOMP.
[33] Xindong Wu,et al. Improving Label Quality in Crowdsourcing Using Noise Correction , 2015, CIKM.
[34] Heng Ji,et al. FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation , 2015, KDD.
[35] Stephen J. Roberts,et al. Dynamic Bayesian Combination of Multiple Imperfect Classifiers , 2012, Decision Making and Imperfection.
[36] Yoav Freund,et al. An Adaptive Version of the Boost by Majority Algorithm , 1999, COLT.
[37] Kai Ming Ting,et al. An Instance-weighting Method to Induce Cost-sensitive Trees , 2001 .
[38] Xi Chen,et al. Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing , 2014, J. Mach. Learn. Res..
[39] Rongrong Ji,et al. Visual tracking via weakly supervised learning from multiple imperfect oracles , 2014, Pattern Recognit..
[40] Bo Zhao,et al. Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation , 2014, SIGMOD Conference.
[41] Jennifer G. Dy,et al. Active Learning from Multiple Knowledge Sources , 2012, AISTATS.
[42] Charles D. Mallah,et al. PLANT LEAF CLASSIFICATION USING PROBABILISTIC INTEGRATION OF SHAPE, TEXTURE AND MARGIN FEATURES , 2013 .
[43] Yakov Ben-Haim,et al. Evaluation of Neural Network Robust Reliability Using Information-Gap Theory , 2006, IEEE Transactions on Neural Networks.
[44] Hisashi Kashima,et al. Clustering Crowds , 2013, AAAI.
[45] David J. Hand,et al. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.
[46] Javier R. Movellan,et al. Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise , 2009, NIPS.
[47] Nitesh V. Chawla,et al. Reliable fake review detection via modeling temporal and behavioral patterns , 2017, 2017 IEEE International Conference on Big Data (Big Data).
[48] Gerardo Hermosillo,et al. Learning From Crowds , 2010, J. Mach. Learn. Res..