Improved Classification Rates for Localized Algorithms under Margin Conditions - Ingrid Karin Blaschzyk
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Saadetis 12-18 tööpäeva jooksul
30-päevane tagastamisõigus
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, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guara ... Täielik kirjeldus
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Kirjeldus
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, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance.
Lisateave
| Autor | Ingrid Karin Blaschzyk |
|---|---|
| Kirjastaja | Springer Fachmedien Wiesbaden |
| Väljalaskeaasta | 2020 |
| Kaanetüüp | Pehme kaanega |
| EAN | 9783658295905 |