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

Excerpt

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 stan