Name: Improved Classification Rates for Localized Algorithms Under Margin Conditions (en Inglés)
SKU: 52594035
Price: 65.07 USD
Availability: InStock
Author: Ingrid Karin Blaschzyk
ISBN: 9783658295905

portada Improved Classification Rates for Localized Algorithms Under Margin Conditions (en Inglés)

Improved Classification Rates for Localized Algorithms Under Margin Conditions (en Inglés)

Formato

Libro Físico

Editorial

Springer Spektrum

Autor

Ingrid Karin Blaschzyk

Año

2020

Idioma

Inglés

N° páginas

141

Encuadernación

Tapa Blanda

ISBN13

9783658295905

N° edición

Categorías

Matemáticas aplicadas

Improved Classification Rates for Localized Algorithms Under Margin Conditions (en Inglés)

Ingrid Karin Blaschzyk (Autor) · Springer Spektrum · Tapa Blanda

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Reseña del libro "Improved Classification Rates for Localized Algorithms Under Margin Conditions (en Inglés)"

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.