Summary of Wave-rvfl: a Randomized Neural Network Based on Wave Loss Function, by M. Sajid et al.
Wave-RVFL: A Randomized Neural Network Based on Wave Loss Function
by M. Sajid, A. Quadir, M. Tanveer
First submitted to arxiv on: 5 Aug 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 random vector functional link (RVFL) network has been praised for its excellent generalization capabilities in machine learning. However, its reliance on the square loss function makes it vulnerable to noise and outliers. Moreover, calculating RVFL’s unknown parameters requires matrix inversion of the entire training sample, limiting its scalability. To address these issues, the Wave-RVFL model was proposed, incorporating the wave loss function. The optimization problem was solved using the adaptive moment estimation (Adam) algorithm, eliminating the need for matrix inversion and enhancing scalability. By preventing over-penalization of deviations, Wave-RVFL exhibits robustness against noise and outliers, striking a balance between managing noise and outliers. The model’s performance and robustness were evaluated on multiple UCI datasets with and without added noise and outliers across various domains and sizes. Results showed superior performance and robustness compared to baseline models, making Wave-RVFL an effective and scalable classification solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The random vector functional link (RVFL) network is a type of machine learning model that’s good at predicting things. But it has some problems – it gets upset by noise or things that don’t quite fit the pattern. It also needs to do a lot of complicated math to figure out how it works, which makes it slow and hard to use. To fix these issues, scientists came up with a new version called Wave-RVFL. This model uses a different way of measuring mistakes, called the wave loss function. They used an algorithm called Adam to make it work, and this made it faster and more efficient. The new model is also better at dealing with noise and things that don’t fit the pattern. Scientists tested Wave-RVFL on lots of different datasets and found that it works really well. |
Keywords
» Artificial intelligence » Classification » Generalization » Loss function » Machine learning » Optimization
