Summary of Multiparameter Regularization and Aggregation in the Context Of Polynomial Functional Regression, by Elke R. Gizewski and Markus Holzleitner and Lukas Mayer-suess and Sergiy Pereverzyev Jr. and Sergei V. Pereverzyev
Multiparameter regularization and aggregation in the context of polynomial functional regression
by Elke R. Gizewski, Markus Holzleitner, Lukas Mayer-Suess, Sergiy Pereverzyev Jr., Sergei V. Pereverzyev
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Numerical Analysis (math.NA); Statistics Theory (math.ST)
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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 In this study, researchers in polynomial functional regression move beyond single-parameter regularization schemes to develop an algorithm for multiple parameter regularization. The proposed method enables the aggregation of models with varying regularization parameters, which is theoretically grounded and experimentally validated on both synthetic and real-world medical data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new approach to polynomial functional regression that goes beyond traditional single-parameter regularization methods. By developing an algorithm for multiple parameter regularization, the authors show how to combine models with different regularization parameters in a way that’s both theoretically sound and practically effective. The method is tested on both artificial and real-world medical datasets, with promising results. |
Keywords
» Artificial intelligence » Regression » Regularization
