Improved Classification Rates for Localized Algorithms under Margin Conditions
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Improved Classification Rates for Localized Algorithms under Margin Conditions “
Introduction to Statistical Learning Theory.- Histogram Rule: Oracle Inequality and Learning Rates.- Localized SVMs: Oracle Inequalities and Learning Rates.
Autoren-Porträt von Ingrid Karin Blaschzyk
Ingrid Karin Blaschzyk is a postdoctoral researcher in the Department of Mathematics at the University of Stuttgart, Germany.
Bibliographische Angaben
- Autor: Ingrid Karin Blaschzyk
- 2020, 1st ed. 2020, XV, 126 Seiten, 126 farbige Abbildungen, Masse: 14,7 x 20,9 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3658295902
- ISBN-13: 9783658295905
Sprache:
Englisch
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