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Improved Classification Rates for Localized Algorithms under Margin Conditions

Authors: Blaschzyk, Ingrid

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  • Study in the field of natural sciences
  • Study in the field of statistical learning theory
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  • ISBN 978-3-658-29591-2
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About this book

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.

About the authors

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

Table of contents (5 chapters)

Table of contents (5 chapters)

Buy this book

eBook $44.99
price for USA in USD (gross)
  • ISBN 978-3-658-29591-2
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $59.99
price for USA in USD
  • ISBN 978-3-658-29590-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Improved Classification Rates for Localized Algorithms under Margin Conditions
Authors
Copyright
2020
Publisher
Springer Spektrum
Copyright Holder
Springer Fachmedien Wiesbaden GmbH, part of Springer Nature
eBook ISBN
978-3-658-29591-2
DOI
10.1007/978-3-658-29591-2
Softcover ISBN
978-3-658-29590-5
Edition Number
1
Number of Pages
XV, 126
Number of Illustrations
5 illustrations in colour
Topics