Snacks: a fast large-scale kernel SVM solver
(2023) 2023 European Control Conference (ECC) — Location: Bucharest, Romania (13.June.2023)
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- Abstract
- Kernel methods provide a powerful framework for non parametric learning. They are based on kernel functions and allow learning in a rich functional space while applying linear statistical learning tools, such as Ridge Regression or Support Vector Machines. However, standard kernel methods suffer from a quadratic time and memory complexity in the number of data points and thus have limited applications in large-scale learning. In this paper, we propose Snacks, a new large-scale solver for Kernel Support Vector Machines. Specifically, Snacks relies on a Nyström approximation of the kernel matrix and an accelerated variant of the stochastic subgradient method. We demonstrate formally through a detailed empirical evaluation, that it competes with other SVM solvers on a variety of benchmark datasets.
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Citations
Tanji, S., Vecchia, A. D., Glineur, F., & Villa, S. (2023). Snacks: a fast large-scale kernel SVM solver. IEEE Xplore. Published. 2023 European Control Conference (ECC), Bucharest, Romania. https://doi.org/10.23919/ecc57647.2023.10178323