A Unified View on Multi-class Support Vector Classification

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It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners (2020, background): “We instead opt for multiclass hinge loss (Weston and Watkins, 1999; Dogan et al., 2016) and minimize: ∑”
On Structured Prediction Theory with Calibrated Convex Surrogate Losses (2017, methodology): “Recently, Doğan et al. (2016) has categorized different versions of multi-class SVM and analyze them from Fisher and universal consistency point of views.”
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches (2020, unknown): “It is widely used for the remote pattern recognition and classification problems to successfully execute multiple classification processes [26,27], and ii.”
Cross-Entropy Loss Functions: Theoretical Analysis and Applications (2023, unknown): “…range of contexts, including multi-class classification (Zhang, 2004b; Chen & Sun, 2006; Chen & Xiang, 2006; Tewari & Bartlett, 2007; Liu, 2007; Dogan et al., 2016; Wang & Scott, 2020; Ramaswamy & Agarwal, 2012; Narasimhan et al., 2015; Agarwal & Agarwal, 2015; Williamson et al., 2016;…”