Abstract

Several decades ago, SupportVector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other supervised methods and remain one of the most popular approaches in the machine learning arena. In this work, we investigate the training of SVMs through a smooth sparse-promoting-regularized squared hinge loss minimization. This choice paves the way to the application of quick training methods built on majorization-minimization approaches, benefiting from the Lipschitz differentiabililty of the loss function. Moreover, the proposed approach allows us to handle sparsity-preserving regularizers promoting the selection of the most significant features, so enhancing the performance. Numerical tests and comparisons conducted on three different datasets demonstrate the good performance of the proposed methodology in terms of qualitative metrics (accuracy, precision, recall, and F1 score) as well as computational cost.
Original languageEnglish
Title of host publicationAdvanced Techniques in Optimization for Machine Learning and Imaging
Place of PublicationSpringer, Singapore
PublisherSpringer, Singapore
Pages31-54
Number of pages23
Volume61
ISBN (Electronic)978-981-97-6769-4
ISBN (Print)978-981-97-6768-7
DOIs
Publication statusPublished - 3 Oct 2024

Publication series

NameSpringer INdAM Series
PublisherSpringer Singapore
Volume61
ISSN (Print)2281-518X
ISSN (Electronic)2281-5198

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