3 citations · 4 across the 2 of their papers we have counts for
3 papers
math.ST2019★ 3 cited
Improved Classification Rates for Localized SVMs
Ingrid Blaschzyk, Ingo Steinwart
Localized support vector machines solve SVMs on many spatially defined small chunks and one of their main characteristics besides the computational benefit compared to global SVMs…
stat.ML2016★ 1 cited
Spatial Decompositions for Large Scale SVMs
Philipp Thomann, Ingrid Blaschzyk, Mona Meister +1
Although support vector machines (SVMs) are theoretically well understood, their underlying optimization problem becomes very expensive, if, for example, hundreds of thousands of s…
math.ST2016
Improved Classification Rates under Refined Margin Conditions
Ingrid Blaschzyk, Ingo Steinwart
In this paper we present a simple partitioning based technique to refine the statistical analysis of classification algorithms. The core of our idea is to divide the input space in…