Summary of Convergence Conditions Of Online Regularized Statistical Learning in Reproducing Kernel Hilbert Space with Non-stationary Data, by Xiwei Zhang and Tao Li
Convergence Conditions of Online Regularized Statistical Learning in Reproducing Kernel Hilbert Space With Non-Stationary Data
by Xiwei Zhang, Tao Li
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This research paper explores recursive regularized learning algorithms in reproducing kernel Hilbert spaces (RKHS) when dealing with dependent and non-stationary online data streams. The study focuses on mean square asymptotic stability, introducing the concept of random Tikhonov regularization paths. It shows that these paths can be consistent with unknown functions if certain conditions are met, such as RKHS persistence of excitation. This approach achieves mean square consistency for independent and non-identically distributed data streams when the marginal probability measures are slowly time-varying. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make learning algorithms work well with changing online data. It’s like trying to predict what will happen next based on past information. The researchers used special math tools called reproducing kernel Hilbert spaces (RKHS) and showed that their method can work well if the data changes slowly over time. |
Keywords
* Artificial intelligence * Probability * Regularization
