Convex losses and their applications to SVM, SVR, and Shallow Neural Networks
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Computer Science > Machine Learning
Title:Convex losses and their applications to SVM, SVR, and Shallow Neural Networks
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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.14288 [cs.LG] |
| (or arXiv:2608.14288v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.14288
arXiv-issued DOI via DataCite (pending registration)
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