Improved Classification Rates for Localized Algorithms under Margin Conditions (eBook, PDF)

Improved Classification Rates for Localized Algorithms under Margin Conditions (eBook, PDF)

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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 ...

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