| PRTools contents |
RBLIBSVC
[W,KERNEL,NU] = RBLIBSVC(A)
| Input | |
| A | Dataset |
| Output | |
| W | Mapping: Radial Basis Support Vector Classifier |
| KERNEL | Untrained mapping, representing the optimised kernel |
| NU | Resulting value for NU from NUSVC |
This routine computes a classifier by NULIBSVC using a radial basis kernel with an optimised standard deviation by REGOPTC. The resulting classifier W is identical to NULIBSVC(A,KERNEL,NU). As the kernel optimisation is based on internal cross-validation the dataset A should be sufficiently large. Moreover it is very time-consuming as the kernel optimisation needs about 100 calls to LIBSVC.
mappings, datasets, proxm, libsvc, nulibsvc, regoptc,
| PRTools contents |