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Improved classification rates for localized algorithms under margin conditions
Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems...
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| Lenguaje: | eng |
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Springer
2020
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| Acceso en línea: | https://dx.doi.org/10.1007/978-3-658-29591-2 http://cds.cern.ch/record/2717179 |