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Anotace knihy

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.

Parametry knihy

Zařazení knihy Knihy v angličtině Mathematics & science Mathematics Applied mathematics

• Plný název: Improved Classification Rates for Localized Algorithms under Margin Conditions
• Autor: Ingrid Karin Blaschzyk
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 126
• EAN: 9783658295905
• ID: 25825732
• Nakladatelství: Springer Fachmedien Wiesbaden
• Hmotnost: 454 g
• Rozměry: 210 × 148 × 9 mm
• Datum vydání: 19. March 2020

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