Summary: ALBERT-LUDWIGS-UNIVERSIT ¨AT
FREIBURG
INSTITUT F ¨UR INFORMATIK
Lehrstuhl f¨ur Mustererkennung und Bildverarbeitung
Fast Support Vector Machine Classification of
very large Datasets
Technical Report 2/07
Karina Zapi´en Arreola1
, Janis Fehr and Hans Burkhardt
March, 2007
1now at INSA de Rouen, LITIS
76801 St Etienne du Rouvray, France
Fast Support Vector Machine Classification of
very largeDatasets
Karina Zapi´en Arreola Janis Fehr Hans Burkhardt
March, 2007
Abstract
In many classification applications, Support Vector Machines (SVMs) have proven to
be high performing and easy to handle classifiers with very good generalization abil-
ities. However, one drawback of the SVM is its rather high classification complexity

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

Collections: Computer Technologies and Information Sciences