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15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03)
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
Sacramento, California, USA
November 03-November 05
ISBN: 0-7695-2038-3
ASCII Text
x 
 
Holger Fr?hlich, Olivier Chapelle, Bernhard Sch?lkopf, "Feature Selection for Support Vector Machines by Means of Genetic Algorithms," Tools with Artificial Intelligence, IEEE International Conference on, pp. 142, 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), 2003.
 
 
BibTex
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@article{ 10.1109/TAI.2003.1250182,
author = {Holger Fr?hlich and Olivier Chapelle and Bernhard Sch?lkopf},
title = {Feature Selection for Support Vector Machines by Means of Genetic Algorithms},
journal ={Tools with Artificial Intelligence, IEEE International Conference on},
volume = {0},
year = {2003},
issn = {1082-3409},
pages = {142},
doi = {http://doi.ieeecomputersociety.org/10.1109/TAI.2003.1250182},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Tools with Artificial Intelligence, IEEE International Conference on
TI - Feature Selection for Support Vector Machines by Means of Genetic Algorithms
SN - 1082-3409
SP
EP
A1 - Holger Fr?hlich,
A1 - Olivier Chapelle,
A1 - Bernhard Sch?lkopf,
PY - 2003
KW - null
VL - 0
JA - Tools with Artificial Intelligence, IEEE International Conference on
ER -
 
Holger Fr?hlich, Max-Planck-Institute of Biological Cybernetics
Olivier Chapelle, Max-Planck-Institute of Biological Cybernetics
Bernhard Sch?lkopf, Max-Planck-Institute of Biological Cybernetics
The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new approach is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection.
Citation:
Holger Fr?hlich, Olivier Chapelle, Bernhard Sch?lkopf, "Feature Selection for Support Vector Machines by Means of Genetic Algorithms," ictai, pp.142, 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), 2003
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