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portada Improved Classification Rates for Localized Algorithms Under Margin Conditions
Type
Physical Book
Publisher
Author
Year
2020
Language
English
Pages
141
Format
Paperback
ISBN13
9783658295905
Edition No.
1
Categories

Improved Classification Rates for Localized Algorithms Under Margin Conditions

Ingrid Karin Blaschzyk (Author) · Springer Spektrum · Paperback

Improved Classification Rates for Localized Algorithms Under Margin Conditions - Ingrid Karin Blaschzyk

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Synopsis "Improved Classification Rates for Localized Algorithms Under Margin Conditions "

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.

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