Summary of Gradient Boosted Filters For Signal Processing, by Jose A. Lopez et al.
Gradient Boosted Filters For Signal Processing
by Jose A. Lopez, Georg Stemmer, Hector A. Cordourier
First submitted to arxiv on: 15 May 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 Gradient boosted filters for dynamic data are introduced by replacing traditional decision trees with Hammerstein systems in a novel application of gradient boosted models to signal processing. Building on the Volterra series, this work demonstrates the effective generalizability of gradient boosted filters for dynamic data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gradient boosted filters are used to analyze dynamic data. This is different from how they’re usually used for static tabular data. The new approach uses Hammerstein systems instead of decision trees. It’s based on Volterra series, which provide a theoretical foundation for the application. The paper shows that this approach works well with examples. |
Keywords
» Artificial intelligence » Signal processing
