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ML
2002
ACM
104views Machine Learning» more  ML 2002»
13 years 7 months ago
A Simple Decomposition Method for Support Vector Machines
The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this pape...
Chih-Wei Hsu, Chih-Jen Lin
NIPS
2000
13 years 9 months ago
Feature Selection for SVMs
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This sear...
Jason Weston, Sayan Mukherjee, Olivier Chapelle, M...
EVOW
2006
Springer
13 years 11 months ago
A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data
We propose a Genetic Algorithm (GA) approach combined with Support Vector Machines (SVM) for the classification of high dimensional Microarray data. This approach is associated to ...
Edmundo Bonilla Huerta, Béatrice Duval, Jin...
NIPS
1998
13 years 9 months ago
Dynamically Adapting Kernels in Support Vector Machines
The kernel-parameter is one of the few tunable parameters in Support Vector machines, controlling the complexity of the resulting hypothesis. Its choice amounts to model selection...
Nello Cristianini, Colin Campbell, John Shawe-Tayl...
ICML
2005
IEEE
14 years 8 months ago
An efficient method for simplifying support vector machines
In this paper we describe a new method to reduce the complexity of support vector machines by reducing the number of necessary support vectors included in their solutions. The red...
DucDung Nguyen, Tu Bao Ho