ModelSV {svcR} | R Documentation |
Computes kernel matrix, lagrange coefficients, support vectors and radius
## S4 method for signature 'numeric': ModelSV.compute(x , MatriceKernel = NULL, MatriceK = NULL, Nu = 1, nlin = 1, MaxIter = 2, MaxValA = 2, AroundNull = 0.01, AroundNullVA = 0.01 ) ## S4 method for signature 'matrix': OptimQuadProgWcluster(MatriceKernel , Nu = 1, MaxValA = 2, MinW = 0.0001 )
x |
method of lagrange computation 1 or 2 |
MatriceKernel |
kernel matrix with vector format |
MatriceK |
kernel matrix with vector format |
Nu |
svc parameter |
nlin |
number |
MaxIter |
maximum iteration for coefficients computation |
MaxValA |
number of neigbours on the grid |
AroundNull |
almost null parameter |
AroundNullVA |
almost null parameter for coefficients |
MinW |
min value for coefficients |
An S4 object of class ModelSV
The object is the svc model along with
the slots :
VectorWA |
lagrange coefficients : VectorsYA$A |
RadiusC |
radius of the hypersphere containing data |
SmallR |
residu of radius |
OptimQuadProgWcluster return a list containing lagrange coefficients.
slots can be accessed by object@slot
Nicolas Turenne - INRA France nicolas.turenne@jouy.inra.fr
N.Turenne , Some Heuristics to speed-up Support Vector Clustering , technical report 2006, INRA, France http://migale.jouy.inra.fr/~turenne/svc.pdf
## exemple with iris data # model computing fmc = findModelCluster.Test(); fmc@WVectorsYA$A; # lagrange coefficients