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＜電子ブック＞
Improved Classification Rates for Localized Algorithms under Margin Conditions / by Ingrid Karin Blaschzyk

版	1st ed. 2020.
出版者	(Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum)
出版年	2020
大きさ	XV, 126 p. 5 illus. in color : online resource
著者標目	*Blaschzyk, Ingrid Karin author SpringerLink (Online service)
件　名	LCSH:Mathematics LCSH:Probabilities LCSH:Statistics  FREE:Applications of Mathematics FREE:Probability Theory FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
一般注記	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 HTTP:URL=https://doi.org/10.1007/978-3-658-29591-2

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