Classification in the presence of class noise using a probabilistic Kernel Fisher method

By Yunlei Li, L. Wessels, D. de Ridder, Marcel J. T. Reinders. 2007

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Learning kernel logistic regression in the presence of class label noise (2014, background): “University of Birmingham Learning kernel logistic regression in the presence of class label noise Bootkrajang, Jakramate; Kabán, Ata DOI: 10.1016/j.patcog.2014.05.007 License: Creative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) Document Version Peer reviewed version Citation for publi”
Classification in the Presence of Label Noise: A Survey (2014, methodology): “[251] consider mixtures of Gaussian distributions.”
Robust supervised classification with mixture models: Learning from data with uncertain labels (2009, methodology): “[18] and its extension [19] are designed for only two classes.”
Multi-class classification in the presence of labelling errors (2011, background): “[10] carried out extensive experiments on more complex datasets, which convincingly demonstrated the value of explicit modelling.”
Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs (2011, unknown): “Later, [2] extended this model by relaxing the Gaussian distribution assumption and carried out extensive experiments on more complex datasets, which convincingly demonstrated the value of explicit label noise modeling.”