TY - JOUR
T1 - A novel multi-class SVM model using second-order cone constraints
AU - López, Julio
AU - Maldonado, Sebastián
AU - Carrasco, Miguel
PY - 2016/3/1
Y1 - 2016/3/1
N2 - In this work we present a novel maximum-margin approach for multi-class Support Vector Machines based on second-order cone programming. The proposed method consists of a single optimization model to construct all classification functions, in which the number of second-order cone constraints corresponds to the number of classes. This is a key difference from traditional SVM, where the number of constraints is usually related to the number of training instances. This formulation is extended further to kernel-based classification, while the duality theory provides an interesting geometric interpretation: the method finds an equidistant point between a set of ellipsoids. Experiments on benchmark datasets demonstrate the virtues of our method in terms of predictive performance compared with various other multicategory SVM approaches.
AB - In this work we present a novel maximum-margin approach for multi-class Support Vector Machines based on second-order cone programming. The proposed method consists of a single optimization model to construct all classification functions, in which the number of second-order cone constraints corresponds to the number of classes. This is a key difference from traditional SVM, where the number of constraints is usually related to the number of training instances. This formulation is extended further to kernel-based classification, while the duality theory provides an interesting geometric interpretation: the method finds an equidistant point between a set of ellipsoids. Experiments on benchmark datasets demonstrate the virtues of our method in terms of predictive performance compared with various other multicategory SVM approaches.
KW - Multi-class classification
KW - Second-order cone programming
KW - Support vector machines
KW - Multi-class classification
KW - Second-order cone programming
KW - Support vector machines
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84959238532&origin=inward
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U2 - 10.1007/s10489-015-0712-8
DO - 10.1007/s10489-015-0712-8
M3 - Article
SN - 0924-669X
VL - 44
SP - 457
EP - 469
JO - Applied Intelligence
JF - Applied Intelligence
IS - 2
ER -