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Improved classification rates for lo...

Blaschzyk, Ingrid Karin.

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Improved classification rates for localized algorithms under margin conditions

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improved classification rates for localized algorithms under margin conditions/ by Ingrid Karin Blaschzyk.
作者:	Blaschzyk, Ingrid Karin.
出版者:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2020.,
面頁冊數:	xv, 126 p. :ill., digital ;24 cm.
內容註:	Introduction to Statistical Learning Theory -- Histogram Rule: Oracle Inequality and Learning Rates -- Localized SVMs: Oracle Inequalities and Learning Rates.
Contained By:	Springer eBooks
標題:	Discriminant analysis. -
電子資源:	https://doi.org/10.1007/978-3-658-29591-2
ISBN:	9783658295912

Improved classification rates for localized algorithms under margin conditions
Blaschzyk, Ingrid Karin.

Improved classification rates for localized algorithms under margin conditions[electronic resource] /by Ingrid Karin Blaschzyk. - Wiesbaden :Springer Fachmedien Wiesbaden :2020. - xv, 126 p. :ill., digital ;24 cm.

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

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.

ISBN: 9783658295912

Standard No.: 10.1007/978-3-658-29591-2doiSubjects--Topical Terms:

560438
Discriminant analysis.

LC Class. No.: QA278.65 / .B537 2020

Dewey Class. No.: 519.535

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505 0 $a Introduction to Statistical Learning Theory -- Histogram Rule: Oracle Inequality and Learning Rates -- Localized SVMs: Oracle Inequalities and Learning Rates.
520 $a 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.
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