Summary of Robust Support Vector Machines Via Conic Optimization, by Valentina Cepeda et al.
Robust support vector machines via conic optimization
by Valentina Cepeda, Andrés Gómez, Shaoning Han
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Computation (stat.CO)
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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 learning support vector machines (SVMs) robust to uncertainty is presented, addressing the limitations of traditional loss functions like the hinge loss. By using mixed-integer optimization techniques, a new loss function is derived that approximates the 0-1 loss while preserving convexity. This proposed estimator outperforms standard SVMs with the hinge loss in both outlier-free and outlier-rich regimes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to learn how to make support vector machines (SVMs) more reliable when there’s uncertainty or mistakes in the data. Right now, many loss functions used for SVMs are sensitive to these errors, which can make them perform poorly. Some methods try to use a 0-1 loss function or approximations, but they’re often too complicated and slow. This paper introduces a new way to find a good balance between being robust and efficient using mixed-integer optimization techniques. The results show that this approach is competitive with the usual SVMs in normal situations and even better when there are outliers. |
Keywords
* Artificial intelligence * Hinge loss * Loss function * Optimization
