Improved Classification Rates for Localized Algorithms Under Margin Conditions

Name: Improved Classification Rates for Localized Algorithms Under Margin Conditions
Brand: Springer
SKU: 9783658295905
Price: 3639.0 INR
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by Ingrid Karin Blaschzyk

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ISBN13: 9783658295905
Binding: Paperback
Subject: N/A
Publisher: Springer
Publisher Imprint: Springer Spektrum
Publication Date: March 19, 2020
Pages: 126
Original Price: EUR 49.99
Language: English
Edition: 2020
Item Weight: 195 grams
BISAC Subject(s): Applied and Probability & Statistics / General

About The Book

From the Back Cover
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.

Contents

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

Target Groups

Researchers, students, and practitioners in the fields of mathematics and computer sciences who focus on machine learning or statistical learning theory

The Author

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

About The Author

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

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