Improved Classification Rates for Localized Algorithms under Margin Conditions - Couverture souple

Blaschzyk, Ingrid Karin

Couverture souple

ISBN 10 : 3658295902 ISBN 13 : 9783658295905

Editeur : Springer Spektrum, 2020

Synopsis

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.

A propos du livre

À propos de l'auteur

Ingrid Karin Blaschzyk is a postdoctoral researcher in the Department of Mathematics at the University of Stuttgart, Germany.​

Détails de l'édition

• Éditeur: Springer Spektrum
• Date d'édition: 2020
• Langue: anglais
• ISBN 10: 3658295902
• ISBN 13: 9783658295905
• Reliure: Broché
• Numéro d'édition: 1
• Nombre de pages: 144
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne