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Support Vector Machines for Multi-class Classification
 

Summary: Support Vector Machines
for Multi-class Classification
Eddy Mayoraz and Ethem Alpaydm
IDIAP--Dalle Molle Institute for Perceptual Artificial Intelligence
CP 592, CH-1920 Martigny, Switzerland
Dept of Computer Engineering, Bogazici University TR-80815 Istanbul, Turkey
Abstract: Support vector machines (SVMs) are primarily designed for 2-class clas-
sification problems. Although in several papers it is mentioned that the combination
of K SVMs can be used to solve a K-class classification problem, such a procedure
requires some care. In this paper, the scaling problem of different SVMs is highlighted.
Various normalization methods are proposed to cope with this problem and their effi-
ciencies are measured empirically. This simple way of using SVMs to learn a K-class
classification problem consists in choosing the maximum applied to the outputs of K
SVMs solving a one-per-class decomposition of the general problem. In the second part
of this paper, more sophisticated techniques are suggested. On the one hand, a stack-
ing of the K SVMs with other classification techniques is proposed. On the other end,
the one-per-class decomposition scheme is replaced by more elaborated schemes based
on error-correcting codes. An incremental algorithm for the elaboration of pertinent
decomposition schemes is mentioned, which exploits the properties of SVMs for an
efficient computation.

  

Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University

 

Collections: Computer Technologies and Information Sciences