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Fast Support Vector Machine Classification of Very Large Datasets
 

Summary: Fast Support Vector Machine Classification of
Very Large Datasets
Janis Fehr1
, Karina Zapi´en Arreola2
and Hans Burkhardt1
1
University of Freiburg, Chair of Pattern Recognition and Image Processing
79110 Freiburg, Germany
fehr@informatik.uni-freiburg.de
2
INSA de Rouen, LITIS
76801 St Etienne du Rouvray, France
Abstract. In many classification applications, Support Vector Machines (SVMs)
have proven to be highly performing and easy to handle classifiers with very good
generalization abilities. However, one drawback of the SVM is its rather high classi-
fication complexity which scales linearly with the number of Support Vectors (SVs).
This is due to the fact that for the classification of one sample, the kernel function
has to be evaluated for all SVs. To speed up classification, different approaches have
been published, most which of try to reduce the number of SVs. In our work, which
is especially suitable for very large datasets, we follow a different approach: as we

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences