 
Summary: Support Vector Machines
for Multiclass Classification
Eddy Mayoraz and Ethem Alpaydm
IDIAPDalle Molle Institute for Perceptual Artificial Intelligence
CP 592, CH1920 Martigny, Switzerland
Dept of Computer Engineering, Bogazici University TR80815 Istanbul, Turkey
Abstract: Support vector machines (SVMs) are primarily designed for 2class clas
sification problems. Although in several papers it is mentioned that the combination
of K SVMs can be used to solve a Kclass 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 Kclass
classification problem consists in choosing the maximum applied to the outputs of K
SVMs solving a oneperclass 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 oneperclass decomposition scheme is replaced by more elaborated schemes based
on errorcorrecting codes. An incremental algorithm for the elaboration of pertinent
decomposition schemes is mentioned, which exploits the properties of SVMs for an
efficient computation.
