Summary of Advancing Rvfl Networks: Robust Classification with the Hawkeye Loss Function, by Mushir Akhtar et al.
Advancing RVFL networks: Robust classification with the HawkEye loss function
by Mushir Akhtar, Ritik Mishra, M. Tanveer, Mohd. Arshad
First submitted to arxiv on: 1 Oct 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 The proposed study introduces a novel variant of random vector functional link (RVFL) neural networks, incorporating the HawkEye loss (H-loss) function to enhance robustness against outliers and noise. The H-loss function offers boundedness, smoothness, and an insensitive zone, making it suitable for real-world applications. By embedding the H-loss function into the RVFL framework, the authors develop a novel robust RVFL model termed H-RVFL. This work addresses a significant gap in the literature, as no bounded loss function has been incorporated into RVFL to date. The proposed H-RVFL model’s effectiveness is validated through extensive experiments on 40 benchmark datasets from UCI and KEEL repositories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make neural networks more robust against noisy data. It combines two techniques: the random vector functional link (RVFL) network, which is already good at handling noise, and the HawkEye loss function, which helps by limiting the impact of extreme errors. This combination leads to a new type of neural network called H-RVFL, which performs well even when the data has lots of noise. The researchers tested this approach on 40 different datasets and found that it works really well. |
Keywords
» Artificial intelligence » Embedding » Loss function » Neural network
