Name
Abstract | ||
|---|---|---|
This paper proposes an accelerated decomposition algorithm for the robust support vector machine (SVM). Robust SVM aims at solving the overfitting problem when there is outlier in the training data set, which makes the decision surface less contoured and results in sparse support vectors. Training of the robust SVM leads to a quadratic optimization problem with bound and linear constraint. Osuna p... |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TCSII.2004.824044 | IEEE Transactions on Circuits and Systems II: Express Briefs |
Keywords | Field | DocType |
Acceleration,Robustness,Support vector machines,Quadratic programming,Training data,Support vector machine classification,Constraint optimization,Pattern recognition,Polynomials | Structured support vector machine,Mathematical optimization,Least squares support vector machine,Support vector machine,Algorithm,Relevance vector machine,Overfitting,Quadratic programming,Sequential minimal optimization,Decision boundary,Mathematics | Journal |
Volume | Issue | ISSN |
51 | 5 | 1549-7747 |
Citations | PageRank | References |
12 | 0.79 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenjie J. Hu | 1 | 24 | 1.61 |
| Q. Song | 2 | 65 | 6.02 |