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» Training of Support Vector Machines with Mahalanobis Kernels
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ICANN
2007
Springer
16 years 8 days ago
Selection of Basis Functions Guided by the L2 Soft Margin
Support Vector Machines (SVMs) for classification tasks produce sparse models by maximizing the margin. Two limitations of this technique are considered in this work: firstly, th...
Ignacio Barrio, Enrique Romero, Lluís Belan...
SYNASC
2006
IEEE
95views Algorithms» more  SYNASC 2006»
16 years 3 days ago
Evolutionary Support Vector Regression Machines
Evolutionary support vector machines (ESVMs) are a novel technique that assimilates the learning engine of the state-of-the-art support vector machines (SVMs) but evolves the coef...
Ruxandra Stoean, Dumitru Dumitrescu, Mike Preuss, ...
INFORMATICALT
2007
111views more  INFORMATICALT 2007»
15 years 6 months ago
Oblique Support Vector Machines
In this paper we propose a modified framework of support vector machines, called Oblique Support Vector Machines(OSVMs), to improve the capability of classification. The principl...
Chih-Chia Yao, Pao-Ta Yu
NECO
1998
83views more  NECO 1998»
15 years 5 months ago
Properties of Support Vector Machines
Support Vector Machines (SVMs) perform pattern recognition between two point classes by nding a decision surface determined by certain points of the training set, termed Support V...
Massimiliano Pontil, Alessandro Verri
NIPS
2007
15 years 7 months ago
Parallelizing Support Vector Machines on Distributed Computers
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel ...
Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai...