Improved Classification Rates for Localized Algorithms under Margin Conditions

Name: Improved Classification Rates for Localized Algorithms under Margin Conditions
Brand: Springer Fachmedien Wiesbaden
SKU: 9783658295905
Price: 1147 CZK
Availability: InStock

Autor: Ingrid Karin Blaschzyk

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer Fachmedien Wiesbaden

Dostupnost: Skladem u dodavatele

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Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized d...

Informace o knize

Autor

Ingrid Karin Blaschzyk

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2020

Stránek

126

EAN

9783658295905

Enbook ID

25825732

Vydavatel

Springer Fachmedien Wiesbaden

Hmotnost

454

Rozměry

148 x 210 x 9

Kompletní popis

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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Improved Classification Rates for Localized Algorithms under Margin Conditions

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