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NIPS
2004
15 years 5 months ago
A Topographic Support Vector Machine: Classification Using Local Label Configurations
The standard approach to the classification of objects is to consider the examples as independent and identically distributed (iid). In many real world settings, however, this ass...
Johannes Mohr, Klaus Obermayer
CLUSTER
2006
IEEE
15 years 4 months ago
Distributed File System Virtualization Techniques Supporting On-Demand Virtual Machine Environments for Grid Computing
This paper presents a data management solution which allows fast Virtual Machine (VM) instantiation and efficient run-time execution to support VMs as execution environments in Gri...
Ming Zhao 0002, Jian Zhang 0005, Renato J. O. Figu...
126
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IJON
2008
173views more  IJON 2008»
15 years 4 months ago
Support vector machine classification for large data sets via minimum enclosing ball clustering
Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitabl...
Jair Cervantes, Xiaoou Li, Wen Yu, Kang Li
JMLR
2008
114views more  JMLR 2008»
15 years 4 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 4 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