Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations
Typically, nonlinear Support Vector Machines (SVMs) produce significantly higher classification quality when compared to linear ones but, at the same time, their computational complexity is prohibitive for large-scale datasets: this drawback is essentially related to the necessity to store and manip...
| Autors principals: | , |
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| Format: | Article |
| Idioma: | English |
| Publicat: |
Elsevier
2022-01-01
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| Col·lecció: | EURO Journal on Computational Optimization |
| Matèries: | |
| Accés en línia: | http://www.sciencedirect.com/science/article/pii/S2192440622000223 |