Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification

Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the...

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Veröffentlicht in:	arXiv.org 2024-03
Hauptverfasser:	Carlevaro, Alberto, Teodoro Alamo Cantarero, Dabbene, Fabrizio, Mongelli, Maurizio
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Classification Classifiers Cybersecurity Learning theory Machine learning Robustness Statistical analysis
Online-Zugang:	Volltext
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