Call for paper (IF 7.5)：截止2024年9月30日

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Conformal Prediction and Distribution-Free Uncertainty Quantification This special issue aims to explore innovative research in the rapidly evolving field of Conformal Prediction (CP), focusing on its integration and application within the broader scope of Pattern Recognition. We welcome submissions that demonstrate novel approaches, theoretical insights, and practical applications of CP and its role in advancing the field of Pattern Recognition. Topics of interest include, but are not limited to: Theoretical analyses and performance guarantees of CP. Novel CP approaches and conformity measures. Conformal predictive distributions. Conformal change-point and anomaly detection. Venn-Abers and other multiprobability prediction approaches. Post-hoc calibration through CP. Decision-making through CP and distribution-free uncertainty quantification. Implementations of CP frameworks and algorithms. CP for explainable machine learning and Fairness, Accountability and Transparency (FAT). CP app ………………………………