Summary of Effective Non-random Extreme Learning Machine, by Daniela De Canditiis and Fabiano Veglianti
Effective Non-Random Extreme Learning Machine
by Daniela De Canditiis, Fabiano Veglianti
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Extreme Learning Machine (ELM) is a powerful statistical technique used for regression problems. This single-layer neural network randomly samples hidden layer weights from a specific distribution, while output layer weights are learned from data. A key challenge lies in designing the optimal architecture and mitigating sensitivity to random initialization of hidden layer weights. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Extreme Learning Machine (ELM) is a way to solve regression problems. It’s like a special kind of neural network that uses random numbers to figure out how much each part contributes to the answer. The problem is that it can be tricky to decide how many parts this network has and that the results can change a lot depending on the starting point. |
Keywords
» Artificial intelligence » Neural network » Regression
