Improved Classification Rates for Localized Algorithms under Margin Conditions
Blaschzyk, Ingrid Karin
Produktnummer:
188fe82ff424ec47a28601c3f4ff651d12
| Autor: | Blaschzyk, Ingrid Karin |
|---|---|
| Themengebiete: | Classification Gaussian Kernel Hinge Loss Histogram Rule Learning Rates Localized SVMs Oracle Inequality Spatial Decomposition Support Vector Machines (SVMs) Tsybakov Noise |
| Veröffentlichungsdatum: | 19.03.2020 |
| EAN: | 9783658295905 |
| Sprache: | Englisch |
| Seitenzahl: | 126 |
| Produktart: | Kartoniert / Broschiert |
| Verlag: | Springer Fachmedien Wiesbaden GmbH |
Produktinformationen "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, 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.
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