Summary of Asymptotic Bayes Risk Of Semi-supervised Learning with Uncertain Labeling, by Victor Leger and Romain Couillet
Asymptotic Bayes risk of semi-supervised learning with uncertain labeling
by Victor Leger, Romain Couillet
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates a semi-supervised classification setting on Gaussian mixture models, where data labels are uncertain rather than strictly labeled. The main goal is to compute the Bayes risk for this model and compare it with the best-known algorithm. By doing so, the study reveals new insights into the behavior of these algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how computers can correctly classify things without being told exactly which ones belong in each group. Instead of giving clear labels, we give them uncertain ones. The scientists want to figure out how well this method works compared to other ways it’s been tried before. They’re trying to find the best approach. |
Keywords
* Artificial intelligence * Classification * Semi supervised
