30 citations · 63 across the 6 of their papers we have counts for
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stat.ML2016★ 10 cited
Learning with Hierarchical Gaussian Kernels
Ingo Steinwart, Philipp Thomann, Nico Schmid
We investigate iterated compositions of weighted sums of Gaussian kernels and provide an interpretation of the construction that shows some similarities with the architectures of d…
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…