Summary of Hawkeye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function, by Mushir Akhtar et al.
HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function
by Mushir Akhtar, M. Tanveer, Mohd. Arshad
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces a novel symmetric loss function called HawkEye loss function for support vector regression (SVR), which is bounded, smooth, and has an insensitive zone. This addresses the limitations of traditional SVR with epsilon-insensitive loss functions when dealing with outliers and noise. The proposed model, HE-LSSVR, combines the HawkEye loss function with the least squares framework and utilizes the adaptive moment estimation (Adam) algorithm for optimization. Experimental results on UCI, synthetic, and time series datasets show that HE-LSSVR outperforms traditional SVR in terms of generalization performance and training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new type of loss function called HawkEye loss function to help support vector regression (SVR) work better with noisy or outlier data. This is important because current methods can get stuck when dealing with these types of issues. The new method uses this special loss function along with another technique called adaptive moment estimation (Adam) to make the model more efficient and effective. By testing the new approach on different types of datasets, researchers found that it was better at making predictions and took less time to train than old methods. |
Keywords
* Artificial intelligence * Generalization * Loss function * Optimization * Regression * Time series
