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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Improved Classification Rates for Localized Algorithms Under Margin Conditions

Improved Classification Rates for Localized Algorithms Under Margin Conditions

Blaschzyk, Ingrid Karin

Paperback

General MathematicsProbability & Statistics

ISBN10: 3658295902
ISBN13: 9783658295905
Publisher: Springer Spektrum
Published: Mar 19 2020
Pages: 126
Weight: 0.40
Height: 0.31 Width: 5.83 Depth: 8.27
Language: English
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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