Summary of A Systems Theoretic Approach to Online Machine Learning, by Anli Du Preez et al.
A Systems Theoretic Approach to Online Machine Learning
by Anli du Preez, Peter A. Beling, Tyler Cody
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 Machine learning research on online learning often neglects the systems-theoretic perspective, focusing instead on algorithm parameters and statistical theory. This paper addresses this gap by introducing a framework for designing online learning systems from a top-down perspective. The framework is based on input-output systems and distinguishes between system structure and behavior. It also formally approaches concept drift as part of the system’s behavior characteristics. A case study in healthcare provider fraud detection using machine learning illustrates the challenges faced in online learning and demonstrates how this framework can be applied to resolve them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online learning is a big deal, especially when it comes to things like detecting fake doctors. Usually, we focus on how algorithms work or what features are important, but that’s not enough. We need to think about the whole system and how it behaves over time. This paper shows us how to do just that by creating a special kind of framework that helps us design online learning systems. It’s like building a house – you need to start with a solid foundation (the system structure) before adding the details (system behavior). The authors use an example from healthcare to show why this matters and how we can apply it. |
Keywords
* Artificial intelligence * Machine learning * Online learning
