Computational Application of Radial Basis Function Neural Networks (RBFNN) I employ radial basis functions in hidden layers, efficiently modeling complex nonlinear relationships in data. Their unique architecture enables accurate function approximation, classification, and regression, making them versatile and effective across multiple domains.
active 2024-02-17 → 2024-02-17 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2024-02-17 to 2024-02-17. Pushes: 2 total, peak 2 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
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Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| edendenis | 2 | 2 | 0 | 0 |
Recent activity
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Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.