267 citations · 813 across the 34 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Kernel Density Estimation for Dynamical Systems
Hanyuan Hang, Ingo Steinwart, Yunlong Feng +1
We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density. Observations drawn from…
A Short Note on the Comparison of Interpolation Widths, Entropy Numbers, and Kolmogorov Widths
Ingo Steinwart
We compare the Kolmogorov and entropy numbers of compact operators mapping from a Hilbert space into a Banach space. We then apply these general findings to embeddings between repr…
Learning theory estimates with observations from general stationary stochastic processes
Hanyuan Hang, Yunlong Feng, Ingo Steinwart +1
This paper investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by \emph{general}, we mean that many stat…