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Ca’ Foscari University of Venice

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Representative Papers

Convex losses and their applications to SVM, SVR, and Shallow Neural Networks

Aug 14, 2026

This study addresses the challenge of enhancing generalization performance in binary classification tasks by proposing a novel convex loss function that integrates pattern correlation. We theoretically demonstrate that this formulation constitutes a generalized extension of standard losses. Methodologically, Particle Swarm Optimization is employed to solve the primal problem, with model performance evaluated via nested cross-validation. A key contribution lies in elucidating the mechanism through which pattern correlation influences generalization. Experimental results indicate that the proposed loss achieves generalization levels comparable to standard benchmarks on small-sample datasets. Consequently, this work provides robust theoretical support and establishes a new paradigm for optimizing classification models in data-scarce scenarios, effectively bridging the gap between correlation-aware learning and practical small-data applications.

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Latest Papers

Convex losses and their applications to SVM, SVR, and Shallow Neural Networks

Aug 14, 2026

This study addresses the challenge of enhancing generalization performance in binary classification tasks by proposing a novel convex loss function that integrates pattern correlation. We theoretically demonstrate that this formulation constitutes a generalized extension of standard losses. Methodologically, Particle Swarm Optimization is employed to solve the primal problem, with model performance evaluated via nested cross-validation. A key contribution lies in elucidating the mechanism through which pattern correlation influences generalization. Experimental results indicate that the proposed loss achieves generalization levels comparable to standard benchmarks on small-sample datasets. Consequently, this work provides robust theoretical support and establishes a new paradigm for optimizing classification models in data-scarce scenarios, effectively bridging the gap between correlation-aware learning and practical small-data applications.

0 citationsRead paper