Error rate control for classification rules in multiclass mixture models
From MaRDI portal
Publication:6637102
DOI10.1515/IJB-2020-0105MaRDI QIDQ6637102
Marie-Laure Martin-Magniette, Vittorio Perduca, Gilles Blanchard, Tristan Mary-Huard
Publication date: 13 November 2024
Published in: The International Journal of Biostatistics (Search for Journal in Brave)
Cites Work
- Title not available (Why is that?)
- Modeling heterogeneity in random graphs through latent space models: a selective review
- Support vector machines with a reject option
- The false discovery rate for statistical pattern recognition
- On the problem of the most efficient tests of statistical hypotheses.
- On the foundations of noise-free selective classification
- Large-scale multiple testing under dependence
- Unsupervised classification for tiling arrays: chip-chip and transcriptome
- Classification with confidence
- Agnostic Pointwise-Competitive Selective Classification
- A Neyman–Pearson Approach to Statistical Learning
- Finite mixture models
- Consistency of plug-in confidence sets for classification in semi-supervised learning
- Classification with reject option
- On optimum recognition error and reject tradeoff
- Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data
- The Elements of Statistical Learning
- A survey on Neyman-Pearson classification and suggestions for future research
This page was built for publication: Error rate control for classification rules in multiclass mixture models
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6637102)