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Summary: Classification with Invariant Distance Substitution Kernels
Bernard Haasdonk
Institute of Mathematics
University of Freiburg
Hermann-Herder-Str. 10
79104 Freiburg, Germany
haasdonk@mathematik.uni-freiburg.de
Hans Burkhardt
Institute of Computer Science
University of Freiburg
Georges-K¨ohler-Allee 52
79110 Freiburg, Germany
burkhardt@informatik.uni-freiburg.de
Abstract
Kernel methods offer a flexible toolbox for pattern analysis and machine learning. A
general class of kernel functions which incorporates known pattern invariances are invariant
distance substitution (IDS) kernels. Instances such as tangent distance or dynamic time-
warping kernels have demonstrated the real world applicability. This motivates the demand
for investigating the elementary properties of the general IDS-kernels. In this paper we
formally state and demonstrate their invariance properties, in particular the adjustability
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