Name
Abstract | ||
|---|---|---|
A new Smooth Support Vector Machine (SSVM) is proposed and is called NSSVM for short. Different from traditional SSVM that
treats perturbation formulation of SVM, NSSVM treats standard 2-norm error soft margin SVM. Different from traditional SSVM
that uses the 2-norm of the Lagrangian multipliers vector to roughly substitute that of the weight of the separating hyperplane,
which makes the obtained smooth model unequal to the primal program; NSSVM takes into account the connotative relation between
the primal and dual program to transform the original program to a new smooth one. Numerical experiments on several UCI datasets
demonstrate that NSSVM has higher precisions than existing methods.
|
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16530-6_32 | artificial intelligence and computational intelligence |
Keywords | Field | DocType |
2-norm error,smooth model,lagrangian multipliers vector,new smooth support,smooth support vector machine,traditional ssvm,primal and dual program.,dual program,connotative relation,original program,uci datasets,new smooth support vector,primal program,2-norm error soft margin svm,vector machine,support vector machine | Lagrange multiplier,Computer science,Support vector machine,Algorithm,Artificial intelligence,Hyperplane,Machine learning | Conference |
Volume | ISSN | ISBN |
6319 | 16113349 | 3-642-16529-X |
Citations | PageRank | References |
2 | 0.41 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinjin Liang | 1 | 67 | 5.63 |
| De Wu | 2 | 2 | 0.41 |