Name
Title | ||
|---|---|---|
General Dimensional Multiple-Output Support Vector Regressions and Their Multiple Kernel Learning. |
Abstract | ||
|---|---|---|
Support vector regression has been considered as one of the most important regression or function approximation methodologies in a variety of fields. In this paper, two new general dimensional multiple output support vector regressions (MSVRs) named SOCPL1 and SOCPL2 are proposed. The proposed methods are formulated in the dual space and their relationship with the previous works is clearly invest... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCYB.2014.2377016 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Kernel,Vectors,Training,Convex functions,Optimization,Support vector machines | Mathematical optimization,Least squares support vector machine,Radial basis function kernel,Kernel embedding of distributions,Support vector machine,Multiple kernel learning,Polynomial kernel,Artificial intelligence,Relevance vector machine,Kernel method,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
45 | 11 | 2168-2267 |
Citations | PageRank | References |
8 | 0.51 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wooyong Chung | 1 | 8 | 0.51 |
| Jisu Kim | 2 | 211 | 28.11 |
| Heejin Lee | 3 | 8 | 0.51 |
| Euntai Kim | 4 | 1472 | 109.36 |