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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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この書籍について

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.

著者について

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)

書籍の購入

イーブック ¥5,615
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-658-29591-2
  • ウォーターマーク付、 DRMフリー
  • ファイル形式: PDF
  • どの電子書籍リーダーからでもすぐにお読みいただけます。
  • ご購入後、すぐにダウンロードしていただけます。
ソフトカバー ¥7,019
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-658-29590-5
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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書誌情報

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
イーブック ISBN
978-3-658-29591-2
DOI
10.1007/978-3-658-29591-2
ソフトカバー ISBN
978-3-658-29590-5
Edition Number
1
Number of Pages
XV, 126
Number of Illustrations
5 illustrations in colour
Topics

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