Misclassification_Error_Control_Via_Probabilistic_Scaling

Version 1.0.1 (1.15 MB) by Alberto Carlevaro

Repository with code corresponding to the paper "Probabilistic Safety Regions via Finite Families of Adjustable Classifiers""

https://github.com/AlbiCarle/MisErrorControl_PS

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Updated 16 Apr 2025

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Alberto Carlevaro (2025). Misclassification_Error_Control_Via_Probabilistic_Scaling (https://github.com/AlbiCarle/MisErrorControl_PS), GitHub. Retrieved December 27, 2025.

Carlevaro, Alberto, et al. Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification. arXiv, 2024, doi:10.48550/ARXIV.2403.10368.

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Created with R2024a

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Examples/Platooning

Examples/Synthetic

adjustable_LR

adjustable_SVDD

adjustable_SVM

Versions that use the GitHub default branch cannot be downloaded

Version	Published	Release Notes
1.0.1	16 Apr 2025	Updated reference.
1.0.0	5 Sep 2024

To view or report issues in this GitHub add-on, visit the GitHub Repository.

MLA	Carlevaro, Alberto, et al. Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification. arXiv, 2024, doi:10.48550/ARXIV.2403.10368.
APA	Carlevaro, A., Cantarero, T. A., Dabbene, F., & Mongelli, M. (2024). Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification. arXiv. Retrieved from https://arxiv.org/abs/2403.10368
BibTeX	@misc{https://doi.org/10.48550/arxiv.2403.10368, doi = {10.48550/ARXIV.2403.10368}, url = {https://arxiv.org/abs/2403.10368}, author = {Carlevaro, Alberto and Cantarero, Teodoro Alamo and Dabbene, Fabrizio and Mongelli, Maurizio}, keywords = {Machine Learning (stat.ML), Cryptography and Security (cs.CR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.5.3, 68T37 (Primary)}, title = {Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification}, publisher = {arXiv}, year = {2024}, copyright = {Creative Commons Attribution 4.0 International} }

Misclassification_E​rror_Control_Via_Pr​obabilistic_Scaling