Harmless label noise and informative soft-labels in supervised classification
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Publication:2242031
DOI10.1016/j.csda.2021.107253OpenAlexW3156266408MaRDI QIDQ2242031
Geoffrey J. McLachlan, Daniel Ahfock
Publication date: 9 November 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2104.02872
Uses Software
Cites Work
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- Classification with asymmetric label noise: consistency and maximal denoising
- The efficiency of logistic regression compared to normal discriminant analysis under class-conditional classification noise
- Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy
- Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models
- A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise
- Robust supervised classification with mixture models: learning from data with uncertain labels
- Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression
- Maximum likelihood estimation in misspecified generalized linear models
- The Effect of Errors in Diagnosis and Measurement on the Estimation of the Probability of an Event
- The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- The Consistency of Estimators in Finite Mixture Models
- Classification with imperfect training labels
- Asymptotic Results for Discriminant Analysis When the Initial Samples Are Misclassified
- Model-Based Clustering and Classification for Data Science
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