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

DOI10.1016/j.patcog.2007.05.006zbMath1123.68363OpenAlexW2137446405WikidataQ126262149 ScholiaQ126262149MaRDI QIDQ996411

Dick De Ridder, Lodewyk F. A. Wessels, Marcel J. T. Reinders, Yunlei Li

Publication date: 14 September 2007

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2007.05.006

zbMATH Keywords

classificationclass noisekernel Fisher discriminantlabeling noise

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (5)

Estimating mutual information for feature selection in the presence of label noise ⋮ Learning kernel logistic regression in the presence of class label noise ⋮ The efficiency of logistic regression compared to normal discriminant analysis under class-conditional classification noise ⋮ Robust supervised classification with mixture models: learning from data with uncertain labels ⋮ Learning from partially supervised data using mixture models and belief functions

Cites Work

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• Support vector data description
• Noise-tolerant Occam algorithms and their applications to learning decision trees
• An Experiment with the Edited Nearest-Neighbor Rule
• Asymptotic Properties of Nearest Neighbor Rules Using Edited Data

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