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» Dynamically Adapting Kernels in Support Vector Machines
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NIPS
2003
13 years 9 months ago
1-norm Support Vector Machines
The standard 2-norm SVM is known for its good performance in twoclass classi£cation. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advanta...
Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibs...
WCE
2007
13 years 9 months ago
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...
PAMI
2010
132views more  PAMI 2010»
13 years 6 months ago
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
—Adapting the hyperparameters of support vector machines (SVMs) is a challenging model selection problem, especially when flexible kernels are to be adapted and data are scarce....
Tobias Glasmachers, Christian Igel
ICML
2005
IEEE
14 years 9 months ago
Adapting two-class support vector classification methods to many class problems
A geometric construction is presented which is shown to be an effective tool for understanding and implementing multi-category support vector classification. It is demonstrated ho...
Simon I. Hill, Arnaud Doucet
IJNSEC
2006
112views more  IJNSEC 2006»
13 years 8 months ago
An Access Control System with Time-constraint Using Support Vector Machines
Access control is an important issue in information security. It is a necessary mechanism for protecting data in a computer system. In this paper, we apply support vector machines...
Chin-Chen Chang, Iuon-Chang Lin, Chia-Te Liao