aiqbench analysis
This paper introduces novel convex loss functions designed to enhance generalization in Support Vector Machines and shallow neural networks by incorporating pattern correlations directly into the optimization objective. While theoretical analysis suggests potential improvements, empirical results on small datasets indicate that these modified losses yield performance metrics equivalent to standard approaches under nested cross-validation procedures. The study highlights a gap between theoretical enhancements via Particle Swarm Optimization and practical gains observed in current binary classification tasks.
Convex losses and their applications to SVM, SVR, and Shallow Neural Networks
arXiv:2608.14288v1 Announce Type: new Abstract: We propose multiple new convex losses for SVM and Neural Networks, applied to binary classification tasks. While there are practical limitations in exploiting them with the dual SVM models, we are able to use them with SVM primal formulation and Neural Networks. In detail, the primal SVM problem with the modified losses has been solved with the Particle Swarm Optimization algorithm. We prove that the proposed losses are a generalization of the standard loss, and we experiment them with several small data-sets. This preliminary study shows that using pattern correlations inside the loss function could in theory enhance the generalization performances on some data-sets. To evaluate the performance of each loss, we adopt a Nested Cross-Validation procedure. Results show that generalization measures are the same with or without the new losses.
Abstract:We propose multiple new convex losses for SVM and Neural Networks, applied to binary classification tasks. While there are practical limitations in exploiting them with the dual SVM models, we are able to use them with SVM primal formulation and Neural Networks. In detail, the primal SVM problem with the modified losses has been solved with the Particle Swarm Optimization algorithm. We prove that the proposed losses are a generalization of the standard loss, and we experiment them with several small data-sets. This preliminary study shows that using pattern correlations inside the loss function could in theory enhance the generalization performances on some data-sets. To evaluate the performance of each loss, we adopt a Nested Cross-Validation procedure. Results show that generalization measures are the same with or without the new losses.
Machine Learning (cs.LG)
arXiv:2608.14288 [cs.LG]
(or arXiv:2608.14288v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.14288 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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