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.