In this contribution we describe a novel classification approach for
on-line handwriting recognition. The technique combines dynamic time
warping (DTW) and support vector machines (SVMs) by establishing
a new SVM kernel. We call this kernel Gaussian DTW (GDTW) kernel.
This kernel approach has a main advantage over common HMM techniques.
It does not assume a model for the generative class conditional densities.
Instead, it directly addresses the problem of discrimination by creating
class boundaries and thus is less sensitive to modeling assumptions.
By incorporating DTW in the kernel function, general classification
problems with variable-sized sequential data can be handled. In this
respect the proposed method can be straightforwardly applied to all
classification problems, where DTW gives a reasonable distance measure,
e.g.~speech recognition or genome processing. We show experiments
with this kernel approach on the UNIPEN handwriting data, achieving
results comparable to an HMM-based technique.
%0 Generic
%1 Bahlmann2002
%A Bahlmann, C.
%A Haasdonk, Bernard
%A Burkhardt, H.
%B Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition
%D 2002
%I IEEE Computer Society
%K imported test
%P 49--54
%T On-line Handwriting Recognition with Support Vector Machines - A Kernel Approach
%X In this contribution we describe a novel classification approach for
on-line handwriting recognition. The technique combines dynamic time
warping (DTW) and support vector machines (SVMs) by establishing
a new SVM kernel. We call this kernel Gaussian DTW (GDTW) kernel.
This kernel approach has a main advantage over common HMM techniques.
It does not assume a model for the generative class conditional densities.
Instead, it directly addresses the problem of discrimination by creating
class boundaries and thus is less sensitive to modeling assumptions.
By incorporating DTW in the kernel function, general classification
problems with variable-sized sequential data can be handled. In this
respect the proposed method can be straightforwardly applied to all
classification problems, where DTW gives a reasonable distance measure,
e.g.~speech recognition or genome processing. We show experiments
with this kernel approach on the UNIPEN handwriting data, achieving
results comparable to an HMM-based technique.
@misc{Bahlmann2002,
abstract = {In this contribution we describe a novel classification approach for
on-line handwriting recognition. The technique combines dynamic time
warping (DTW) and support vector machines (SVMs) by establishing
a new SVM kernel. We call this kernel \emph{Gaussian DTW (GDTW) kernel}.
This kernel approach has a main advantage over common HMM techniques.
It does not assume a model for the generative class conditional densities.
Instead, it directly addresses the problem of discrimination by creating
class boundaries and thus is less sensitive to modeling assumptions.
By incorporating DTW in the kernel function, general classification
problems with variable-sized sequential data can be handled. In this
respect the proposed method can be straightforwardly applied to all
classification problems, where DTW gives a reasonable distance measure,
e.g.~speech recognition or genome processing. We show experiments
with this kernel approach on the UNIPEN handwriting data, achieving
results comparable to an HMM-based technique.},
added-at = {2021-09-29T11:10:03.000+0200},
author = {Bahlmann, C. and Haasdonk, Bernard and Burkhardt, H.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/23874edf7c8a589ba2c2fae2a03de987f/droessler},
booktitle = {Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition},
file = {:PDF/Bahlmann2002_www_IWFHR2002.pdf:PDF},
groups = {haasdonk, haasdonk_all_papers},
html = {\htmladdnormallink{{\sl Get the pdf file here}} {ftp://ftp.informatik.uni-freiburg.de/papers/lmb/ba_ha_bu_iwfhr02.pdf}},
interhash = {3616d7617a1c5d39450985588ae91b44},
intrahash = {3874edf7c8a589ba2c2fae2a03de987f},
keywords = {imported test},
owner = {haasdonk},
pages = {49--54},
publisher = {IEEE Computer Society},
timestamp = {2021-09-29T09:10:21.000+0200},
title = {{On-line Handwriting Recognition with Support Vector Machines - A Kernel Approach}},
year = 2002
}