Summary of A Critical Analysis Of the Theoretical Framework Of the Extreme Learning Machine, by Irina Perfilievaa et al.
A Critical Analysis of the Theoretical Framework of the Extreme Learning Machine
by Irina Perfilievaa, Nicolas Madrid, Manuel Ojeda-Aciego, Piotr Artiemjew, Agnieszka Niemczynowicz
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 The paper challenges the mathematical foundation of the Extreme Learning Machine (ELM) by refuting two main proofs and creating a dataset that serves as a counterexample to the algorithm. The authors also provide alternative statements that justify the efficiency of ELM in certain theoretical cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper examines the underlying principles of the successful Extreme Learning Machine (ELM). By showing flaws in the mathematical proofs, the study reveals that ELM’s learning process doesn’t have a solid basis. Instead, it creates a dataset with examples where ELM fails and offers alternative explanations for its efficiency. |
