Ingrid Karin Blaschzyk

Improved Classification Rates for Localized Algorithms under Margin Conditions

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Springer Fachmedien Wiesbaden / Springer Spektrum / Springer, Berlin
• Artikelnr. des Verlages: 978-3-658-29590-5
• 1st ed. 2020
• Seitenzahl: 144
• Erscheinungstermin: 19. März 2020
• Englisch
• Abmessung: 210mm x 148mm x 9mm
• Gewicht: 194g
• ISBN-13: 9783658295905
• ISBN-10: 3658295902
• Artikelnr.: 58662214

Autorenporträt

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

Inhaltsangabe

Introduction to Statistical Learning Theory.- Histogram Rule: Oracle Inequality and Learning Rates.- Localized SVMs: Oracle Inequalities and Learning Rates.