Tangent Distance Kernels for Support Vector Machines
B. Haasdonk, and D. Keysers. Proceedings of the 16th International Conference on Pattern Recognition, 2, page 864-868. IEEE Computer Society, (2002)
Abstract
When dealing with pattern recognition problems one encounters different
types of a-priori knowledge. It is important to incorporate such
knowledge into the classification method at hand. A very common type
of a-priori knowledge is transformation invariance of the input data,
e.g.\ geometric transformations of image-data like shifts, scaling
etc. Distance based classification methods can make use of this by
a modified distance measure called tangent distance 13, 14. We
introduce a new class of kernels for support vector machines which
incorporate tangent distance and therefore are applicable in cases
where such transformation invariances are known. We report experimental
results which show that the performance of our method is comparable
to other state-of-the-art methods, while problems of existing ones
are avoided.
%0 Conference Paper
%1 haasdonk2002tangent
%A Haasdonk, B.
%A Keysers, D.
%B Proceedings of the 16th International Conference on Pattern Recognition
%D 2002
%I IEEE Computer Society
%K from:mhartmann ians imported vorlaeufig
%P 864-868
%T Tangent Distance Kernels for Support Vector Machines
%V 2
%X When dealing with pattern recognition problems one encounters different
types of a-priori knowledge. It is important to incorporate such
knowledge into the classification method at hand. A very common type
of a-priori knowledge is transformation invariance of the input data,
e.g.\ geometric transformations of image-data like shifts, scaling
etc. Distance based classification methods can make use of this by
a modified distance measure called tangent distance 13, 14. We
introduce a new class of kernels for support vector machines which
incorporate tangent distance and therefore are applicable in cases
where such transformation invariances are known. We report experimental
results which show that the performance of our method is comparable
to other state-of-the-art methods, while problems of existing ones
are avoided.
@inproceedings{haasdonk2002tangent,
abstract = {When dealing with pattern recognition problems one encounters different
types of a-priori knowledge. It is important to incorporate such
knowledge into the classification method at hand. A very common type
of a-priori knowledge is transformation invariance of the input data,
e.g.\ geometric transformations of image-data like shifts, scaling
etc. Distance based classification methods can make use of this by
a modified distance measure called tangent distance [13, 14]. We
introduce a new class of kernels for support vector machines which
incorporate tangent distance and therefore are applicable in cases
where such transformation invariances are known. We report experimental
results which show that the performance of our method is comparable
to other state-of-the-art methods, while problems of existing ones
are avoided.},
added-at = {2018-07-20T10:55:15.000+0200},
author = {Haasdonk, B. and Keysers, D.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/28ea852793c0b960ea40a203782f0c0f6/mathematik},
booktitle = {Proceedings of the 16th International Conference on Pattern Recognition},
html = {\htmladdnormallink{{\sl Get the PDF file here}} {ftp://ftp.informatik.uni-freiburg.de/papers/lmb/Haasdonk2002_Tangent_Distance_Kernels_for_SVM.pdf}},
interhash = {5a4cee72935946baf4afe8ca0f669f30},
intrahash = {8ea852793c0b960ea40a203782f0c0f6},
keywords = {from:mhartmann ians imported vorlaeufig},
owner = {haasdonk},
pages = {864-868},
publisher = {IEEE Computer Society},
timestamp = {2019-12-18T14:37:55.000+0100},
title = {Tangent Distance Kernels for Support Vector Machines},
volume = 2,
year = 2002
}