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ML
2007
ACM
144views Machine Learning» more  ML 2007»
13 years 4 months ago
Invariant kernel functions for pattern analysis and machine learning
In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invarianc...
Bernard Haasdonk, Hans Burkhardt
GFKL
2007
Springer
164views Data Mining» more  GFKL 2007»
13 years 8 months ago
Classification with Invariant Distance Substitution Kernels
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 d...
Bernard Haasdonk, Hans Burkhardt
SCIA
2005
Springer
137views Image Analysis» more  SCIA 2005»
13 years 10 months ago
Invariance in Kernel Methods by Haar-Integration Kernels
Abstract. We address the problem of incorporating transformation invariance in kernels for pattern analysis with kernel methods. We introduce a new class of kernels by so called Ha...
Bernard Haasdonk, A. Vossen, Hans Burkhardt
ICASSP
2011
IEEE
12 years 8 months ago
Theoretical analyses on a class of nested RKHS's
One of central topics of kernel machines in the field of machine learning is a model selection, especially a selection of a kernel or its parameters. In our previous work, we dis...
Akira Tanaka, Hideyuki Imai, Mineichi Kudo, Masaak...
ICPR
2006
IEEE
14 years 5 months ago
Graph-based transformation manifolds for invariant pattern recognition with kernel methods
We present here an approach for applying the technique of modeling data transformation manifolds for invariant learning with kernel methods. The approach is based on building a ke...
Alexei Pozdnoukhov, Samy Bengio