A simplified approach for efficiency analysis of machine learning algorithms
Muthuramalingam Sivakumar, Sudhaman Parthasarathy, Thiyagarajan Padmapriya · 2024
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Description
Muthuramalingam Sivakumar, Sudhaman Parthasarathy, and Thiyagarajan Padmapriya propose a scoring method in a 2024 paper on machine learning efficiency. Speed and memory matter when models must run on tight hardware or in real time, yet efficiency is often judged by feel. Their framework turns raw measures into one composite score.
Training time, prediction time, memory use, and resource load are gathered, scaled, and weighted by a structured ranking method. The framework was tried on medical image data and on crop prediction data. In the medical case it rewarded robustness; in farming, scalability. The authors offer the score as a shared yardstick for picking and tuning algorithms across fields with unlike demands.
Catalog record
| CREATOR | Muthuramalingam Sivakumar, Sudhaman Parthasarathy, Thiyagarajan Padmapriya |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 67 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |