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JMLR
2008
114views more  JMLR 2008»
15 years 3 months ago
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Problems with sparse features are common in applications such as document classification a...
Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin
PR
2006
229views more  PR 2006»
15 years 3 months ago
FS_SFS: A novel feature selection method for support vector machines
In many pattern recognition applications, high-dimensional feature vectors impose a high computational cost as well as the risk of "overfitting". Feature Selection addre...
Yi Liu, Yuan F. Zheng
TNN
2008
97views more  TNN 2008»
15 years 3 months ago
Training Hard-Margin Support Vector Machines Using Greedy Stagewise Algorithm
Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with this problem by introducing a regularization term...
Liefeng Bo, Ling Wang, Licheng Jiao
TNN
2008
152views more  TNN 2008»
15 years 3 months ago
Distributed Parallel Support Vector Machines in Strongly Connected Networks
We propose a distributed parallel support vector machine (DPSVM) training mechanism in a configurable network environment for distributed data mining. The basic idea is to exchange...
Yumao Lu, Vwani P. Roychowdhury, L. Vandenberghe
CVPR
2011
IEEE
14 years 6 months ago
Support Tucker Machines
In this paper we address the two-class classification problem within the tensor-based framework, by formulating the Support Tucker Machines (STuMs). More precisely, in the propos...
Irene Kotsia, Ioannis Patras