Summary of Firal: An Active Learning Algorithm For Multinomial Logistic Regression, by Youguang Chen and George Biros
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
by Youguang Chen, George Biros
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
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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 paper investigates active learning for multiclass classification using multinomial logistic regression. The authors prove that a Fisher Information Ratio (FIR) bounds the excess risk, then propose an algorithm that minimizes regret to optimize FIR. They conduct experiments on synthetic datasets and find their scheme outperforms five other methods in terms of classification error. Experiments are also performed on MNIST, CIFAR-10, and 50-class ImageNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a new way to learn from data, called pool-based active learning. The goal is to make better predictions for many different categories (multiclass). The authors develop an algorithm that uses something called regret minimization to improve the accuracy of predictions. They test their approach on some artificial datasets and also compare it to other methods using real-world image datasets like MNIST and CIFAR-10. |
Keywords
» Artificial intelligence » Active learning » Classification » Logistic regression
