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| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2025
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.5281/zenodo.15948128 |
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Inhaltsangabe:
- <p><strong>BoostNet-WB</strong> is a practical modification of the AdaBoost algorithm that integrates compact neural networks as weak learners. Instead of classical reweighting, the method employs a weighted bootstrap resampling strategy to reflect example importance during stochastic gradient descent — without modifying standard loss functions or optimizers.</p> <p>The approach includes dynamically adjustable network architectures and regularization techniques to prevent overfitting while preserving weak learner behavior. Experimental results across synthetic datasets show that BoostNet-WB consistently outperforms classical decision stump-based AdaBoost in classification performance, and the adaptive version achieves a favorable trade-off between accuracy and training time.</p> <p>This work highlights the feasibility of neural networks in boosting frameworks and offers a foundation for future extensions, including deeper or convolutional architectures.</p>