Summary of Esa: Example Sieve Approach For Multi-positive and Unlabeled Learning, by Zhongnian Li et al.
ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning
by Zhongnian Li, Meng Wei, Peng Ying, Xinzheng Xu
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach to address the problem of learning from Multi-Positive and Unlabeled (MPU) data is presented in this paper. The authors propose an Example Sieve Approach (ESA) to select examples for training a multi-class classifier, alleviating the risk of shifting minimum risk particularly when models are very flexible. ESA utilizes Certain Loss (CL) values to sieve out some examples during the training stage and analyzes the consistency of the proposed risk estimator. Experimental results on various real-world datasets demonstrate that the proposed approach outperforms previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to learn from data that has many positive examples, but no labels for some of them. This is important because it can help with tasks like image classification or natural language processing. The authors suggest a new method called Example Sieve Approach (ESA) that helps to pick the right examples to use when training a model. They also show that this approach works well on real-world datasets. |
Keywords
» Artificial intelligence » Image classification » Natural language processing
